Endpunkte der Bluttest API

Produktionsendpunkt für die Bluttestanalyse. Laden Sie ein oder mehrere Bluttestbilder oder eine PDF-Datei hoch und erhalten Sie strukturierte Parameter, Patienten- und Labor-Metadaten sowie eine vollständige klinische Interpretation in einer der 100 unterstützten Sprachen. Verbraucht 1 Credit pro Anfrage.

Anfrageparameter

cURL-Beispiel

curl -X POST "https://app.aibloodtestinterpret.com/api/v12/18-09-2026/analyze" \
  -F "username=IHR_BENUTZERNAME" \
  -F "password=IHR_PASSWORT" \
  -F "language=de" \
  -F "[email protected]"

Python-Beispiel

import requests

def bluttest_analysieren(dateipfade, benutzername, passwort, sprache="de"):
    """
    Bluttest mit Kantesti Bluttest-Analyse v12 analysieren.

    Args:
        dateipfade: Ein oder mehrere Pfade zu Bluttestbildern oder eine einzelne PDF
        benutzername: API-Benutzername
        passwort: API-Passwort
        sprache: Sprachcode des Berichts (Standard: de)

    Returns:
        dict: Strukturierte Parameter, Metadaten und klinische Interpretation
    """
    url = "https://app.aibloodtestinterpret.com/api/v12/18-09-2026/analyze"

    handles = [open(pfad, "rb") for pfad in dateipfade]
    try:
        dateien = [("file", (pfad, handle)) for pfad, handle in zip(dateipfade, handles)]
        daten = {"username": benutzername, "password": passwort, "language": sprache}
        antwort = requests.post(url, files=dateien, data=daten, timeout=300)
        antwort.raise_for_status()
        return antwort.json()
    finally:
        for handle in handles:
            handle.close()

# Anwendungsbeispiel
if __name__ == "__main__":
    ergebnis = bluttest_analysieren(
        dateipfade=["bluttest.pdf"],
        benutzername="ihr_benutzername",
        passwort="ihr_passwort",
        sprache="de"
    )
    print(f"Status: {ergebnis['status']}")
    for parameter in ergebnis["data"]["parameters"]:
        print(f"  {parameter['short_name']}: {parameter['result']} {parameter['unit']} ({parameter['evaluation']})")

Beispielantwort

{
  "status": "success",
  "api_version": "v12",
  "data": {
    "metadata": {
      "patient_name": "Jan Novak",
      "patient_age": "45",
      "patient_sex": "Male",
      "lab_name": "BioLAB Medical Center",
      "lab_city": "Prague",
      "lab_country": "Czech Republic",
      "lab_date": "2026-09-11",
      "results_date": "2026-09-12"
    },
    "parameters": [
      {"short_name": "Glucose", "long_name": "Fasting Blood Glucose", "result": "92", "unit": "mg/dL", "reference_range": "74 - 100", "range_normal_min": 74, "range_normal_max": 100, "type": "range", "evaluation": "normal"},
      {"short_name": "ALT", "long_name": "Alanine aminotransferase", "result": "65", "unit": "U/L", "reference_range": "< 45", "range_normal_min": 7, "range_normal_max": 45, "type": "range", "evaluation": "high"},
      {"short_name": "Creatinine", "long_name": "Creatinine", "result": "0.9", "unit": "mg/dL", "reference_range": "0.7 - 1.2", "range_normal_min": 0.7, "range_normal_max": 1.2, "type": "range", "evaluation": "normal"}
    ],
    "interpretation": [
      {"shortcode": "overview", "item": "Most parameters are within their reference ranges."},
      {"shortcode": "key_findings", "item": "Alanine aminotransferase is above the reference range, which warrants a follow-up liver panel."}
    ]
  },
  "timestamp": "2026-09-18T10:30:00Z"
}

Produktionsendpunkt für Bluttestanalyse. Verbraucht 1 Credit pro Anfrage.

Anfrageparameter

cURL-Beispiel

curl -X POST "https://app.aibloodtestinterpret.com/api/v11/01-06-2025/analyze" \
  -F "username=IHR_BENUTZERNAME" \
  -F "password=IHR_PASSWORT" \
  -F "language=de" \
  -F "[email protected]"

Python-Beispiel

import requests

def bluttest_analysieren(dateipfad: str, benutzername: str, passwort: str, sprache: str = "de"):
    """
    Analysiert eine Bluttestdatei mit der Kantesti-API.

    Args:
        dateipfad: Pfad zur Bluttest-PDF oder Bilddatei
        benutzername: API-Benutzername
        passwort: API-Passwort
        sprache: Antwortsprachcode (Standard: de)

    Returns:
        dict: API-Antwort mit Analyseergebnissen
    """
    url = "https://app.aibloodtestinterpret.com/api/v11/01-06-2025/analyze"

    with open(dateipfad, "rb") as f:
        dateien = {"file": (dateipfad, f, "application/pdf")}
        daten = {
            "username": benutzername,
            "password": passwort,
            "language": sprache
        }

        antwort = requests.post(url, files=dateien, data=daten, timeout=120)
        antwort.raise_for_status()
        return antwort.json()

# Anwendungsbeispiel
if __name__ == "__main__":
    ergebnis = bluttest_analysieren(
        dateipfad="bluttest.pdf",
        benutzername="ihr_benutzername",
        passwort="ihr_passwort",
        sprache="de"
    )
    print(f"Status: {ergebnis['status']}")
    print(f"Gefundene Parameter: {len(ergebnis['data']['parameters'])}")

C++-Beispiel (libcurl)

#include <iostream>
#include <string>
#include <curl/curl.h>

// Callback-Funktion für die Antwortverarbeitung
size_t SchreibCallback(void* inhalt, size_t groesse, size_t nmemb, std::string* ausgabe) {
    ausgabe->append((char*)inhalt, groesse * nmemb);
    return groesse * nmemb;
}

std::string bluttestAnalysieren(const std::string& dateipfad,
                                 const std::string& benutzername,
                                 const std::string& passwort,
                                 const std::string& sprache = "de") {
    CURL* curl = curl_easy_init();
    std::string antwort;

    if (curl) {
        curl_mime* formular = curl_mime_init(curl);
        curl_mimepart* feld;

        // Benutzername hinzufügen
        feld = curl_mime_addpart(formular);
        curl_mime_name(feld, "username");
        curl_mime_data(feld, benutzername.c_str(), CURL_ZERO_TERMINATED);

        // Passwort hinzufügen
        feld = curl_mime_addpart(formular);
        curl_mime_name(feld, "password");
        curl_mime_data(feld, passwort.c_str(), CURL_ZERO_TERMINATED);

        // Sprache hinzufügen
        feld = curl_mime_addpart(formular);
        curl_mime_name(feld, "language");
        curl_mime_data(feld, sprache.c_str(), CURL_ZERO_TERMINATED);

        // Datei hinzufügen
        feld = curl_mime_addpart(formular);
        curl_mime_name(feld, "file");
        curl_mime_filedata(feld, dateipfad.c_str());

        curl_easy_setopt(curl, CURLOPT_URL,
            "https://app.aibloodtestinterpret.com/api/v11/01-06-2025/analyze");
        curl_easy_setopt(curl, CURLOPT_MIMEPOST, formular);
        curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, SchreibCallback);
        curl_easy_setopt(curl, CURLOPT_WRITEDATA, &antwort);
        curl_easy_setopt(curl, CURLOPT_TIMEOUT, 120L);

        CURLcode res = curl_easy_perform(curl);

        curl_mime_free(formular);
        curl_easy_cleanup(curl);
    }
    return antwort;
}

int main() {
    std::string ergebnis = bluttestAnalysieren("bluttest.pdf", "benutzername", "passwort");
    std::cout << ergebnis << std::endl;
    return 0;
}

Postman-Konfiguration

{
  "info": {
    "name": "Kantesti Bluttest-API",
    "schema": "https://schema.getpostman.com/json/collection/v2.1.0/collection.json"
  },
  "item": [
    {
      "name": "Bluttest analysieren",
      "request": {
        "method": "POST",
        "header": [],
        "body": {
          "mode": "formdata",
          "formdata": [
            {"key": "username", "value": "{{api_benutzername}}", "type": "text"},
            {"key": "password", "value": "{{api_passwort}}", "type": "text"},
            {"key": "language", "value": "de", "type": "text"},
            {"key": "file", "type": "file", "src": "/pfad/zur/bluttest.pdf"}
          ]
        },
        "url": {
          "raw": "https://app.aibloodtestinterpret.com/api/v11/01-06-2025/analyze",
          "protocol": "https",
          "host": ["app", "aibloodtestinterpret", "com"],
          "path": ["api", "v11", "01-06-2025", "analyze"]
        }
      }
    }
  ]
}

Beispielantwort

{
  "status": "success",
  "data": {
    "metadata": {
      "patient_name": "Jan Novák",
      "lab_name": "BioLAB Medizinzentrum",
      "lab_city": "München",
      "lab_country": "Deutschland",
      "lab_date": "2025-05-11",
      "patient_age": "45",
      "patient_sex": "Männlich"
    },
    "parameters": [
      {
        "short_name": "WBC",
        "long_name": "Weiße Blutkörperchen",
        "category": "Komplettes Blutbild",
        "result": 7.2,
        "unit": "10^9/L",
        "evaluation": "normal",
        "range_min": 2.0,
        "range_max": 12.0,
        "range_normal_min": 4.0,
        "range_normal_max": 10.0,
        "short_description": "Misst die Gesamtzahl der weißen Blutkörperchen im Blut.",
        "long_description": "Weiße Blutkörperchen (Leukozyten) sind für die Immunfunktion unerlässlich..."
      },
      {
        "short_name": "HGB",
        "long_name": "Hämoglobin",
        "category": "Komplettes Blutbild",
        "result": 14.5,
        "unit": "g/dL",
        "evaluation": "normal",
        "range_min": 10.0,
        "range_max": 18.0,
        "range_normal_min": 13.5,
        "range_normal_max": 17.5,
        "short_description": "Protein in roten Blutkörperchen, das Sauerstoff transportiert.",
        "long_description": "Hämoglobin ist das eisenhaltige Protein, das für den Sauerstofftransport verantwortlich ist..."
      }
    ],
    "interpretation": [
      {
        "title": "Gesamtgesundheitsbewertung",
        "shortcode": "overall_health_assessment",
        "subsections": [
          {
            "subtitle": "Umfassender Überblick",
            "shortcode": "overall_health_assessment_overview",
            "items": [
              {"item": "Der Patient zeigt eine erhaltene Organfunktion mit normalen hämatologischen Parametern."},
              {"item": "Im kompletten Blutbild wurden keine signifikanten Auffälligkeiten festgestellt."}
            ]
          }
        ]
      }
    ]
  },
  "api_version": "v11",
  "timestamp": "2025-12-22T10:30:00Z"
}

Antwortfelder-Referenz

Stammebene

data.metadata Objekt

data.parameters Array-Element

data.interpretation Array-Element

Vollständiges Antwortbeispiel

{
  "status": "success",
  "data": {
    "metadata": {
      "patient_name": "Anna Müller",
      "lab_name": "MedLab Diagnostics International",
      "lab_city": "München",
      "lab_country": "Deutschland",
      "lab_date": "2025-12-15",
      "results_date": "2025-12-16",
      "patient_age": "38",
      "patient_sex": "female"
    },
    "parameters": [
      {
        "short_name": "WBC",
        "long_name": "Leukozytenzahl",
        "category": "Blutbild",
        "result": "6.8",
        "unit": "10^9/L",
        "evaluation": "normal",
        "range_min": "4.0",
        "range_max": "11.0",
        "range_normal_min": "4.5",
        "range_normal_max": "10.0",
        "short_description": "Misst die Gesamtzahl der weißen Blutkörperchen.",
        "long_description": "Weiße Blutkörperchen (Leukozyten) sind wesentliche Bestandteile des Immunsystems..."
      },
      {
        "short_name": "RBC",
        "long_name": "Erythrozytenzahl",
        "category": "Blutbild",
        "result": "4.52",
        "unit": "10^12/L",
        "evaluation": "normal",
        "range_min": "3.8",
        "range_max": "5.8",
        "range_normal_min": "4.0",
        "range_normal_max": "5.5",
        "short_description": "Misst die Gesamtzahl der roten Blutkörperchen.",
        "long_description": "Rote Blutkörperchen (Erythrozyten) transportieren Sauerstoff von der Lunge zu den Körpergeweben..."
      },
      {
        "short_name": "HGB",
        "long_name": "Hämoglobin",
        "category": "Blutbild",
        "result": "13.2",
        "unit": "g/dL",
        "evaluation": "normal",
        "range_min": "11.5",
        "range_max": "16.0",
        "range_normal_min": "12.0",
        "range_normal_max": "15.5",
        "short_description": "Protein in roten Blutkörperchen, das Sauerstoff transportiert.",
        "long_description": "Hämoglobin ist das eisenhaltige Protein in roten Blutkörperchen, das für den Sauerstofftransport verantwortlich ist..."
      },
      {
        "short_name": "HCT",
        "long_name": "Hämatokrit",
        "category": "Blutbild",
        "result": "39.8",
        "unit": "%",
        "evaluation": "normal",
        "range_min": "35.0",
        "range_max": "47.0",
        "range_normal_min": "36.0",
        "range_normal_max": "44.0",
        "short_description": "Prozentsatz des Blutvolumens, der von roten Blutkörperchen eingenommen wird.",
        "long_description": "Der Hämatokrit misst den Anteil des Blutes, der aus roten Blutkörperchen besteht..."
      },
      {
        "short_name": "PLT",
        "long_name": "Thrombozytenzahl",
        "category": "Blutbild",
        "result": "245",
        "unit": "10^9/L",
        "evaluation": "normal",
        "range_min": "150",
        "range_max": "400",
        "range_normal_min": "150",
        "range_normal_max": "350",
        "short_description": "Misst die Anzahl der Blutplättchen im Blut.",
        "long_description": "Thrombozyten (Blutplättchen) sind essentiell für die Blutgerinnung und Wundheilung..."
      },
      {
        "short_name": "GLU",
        "long_name": "Nüchternglukose",
        "category": "Stoffwechselpanel",
        "result": "102",
        "unit": "mg/dL",
        "evaluation": "borderline_high",
        "range_min": "70",
        "range_max": "140",
        "range_normal_min": "70",
        "range_normal_max": "99",
        "short_description": "Misst den Blutzuckerspiegel nach dem Fasten.",
        "long_description": "Die Nüchternglukose ist ein wichtiger Indikator dafür, wie gut der Körper Zucker verstoffwechselt..."
      },
      {
        "short_name": "TC",
        "long_name": "Gesamtcholesterin",
        "category": "Lipidprofil",
        "result": "218",
        "unit": "mg/dL",
        "evaluation": "borderline_high",
        "range_min": "0",
        "range_max": "300",
        "range_normal_min": "0",
        "range_normal_max": "200",
        "short_description": "Misst das Gesamtcholesterin im Blut.",
        "long_description": "Das Gesamtcholesterin ist die Summe aus HDL-, LDL- und VLDL-Cholesterin..."
      },
      {
        "short_name": "HDL",
        "long_name": "HDL-Cholesterin",
        "category": "Lipidprofil",
        "result": "58",
        "unit": "mg/dL",
        "evaluation": "normal",
        "range_min": "35",
        "range_max": "100",
        "range_normal_min": "50",
        "range_normal_max": "90",
        "short_description": "Misst das 'gute' Cholesterin.",
        "long_description": "HDL (High-Density-Lipoprotein) Cholesterin hilft, andere Formen von Cholesterin zu entfernen..."
      },
      {
        "short_name": "LDL",
        "long_name": "LDL-Cholesterin",
        "category": "Lipidprofil",
        "result": "142",
        "unit": "mg/dL",
        "evaluation": "high",
        "range_min": "0",
        "range_max": "200",
        "range_normal_min": "0",
        "range_normal_max": "100",
        "short_description": "Misst das 'schlechte' Cholesterin.",
        "long_description": "LDL (Low-Density-Lipoprotein) Cholesterin kann sich in den Arterienwänden ablagern..."
      },
      {
        "short_name": "TRIG",
        "long_name": "Triglyzeride",
        "category": "Lipidprofil",
        "result": "156",
        "unit": "mg/dL",
        "evaluation": "borderline_high",
        "range_min": "0",
        "range_max": "500",
        "range_normal_min": "0",
        "range_normal_max": "150",
        "short_description": "Misst das Fett im Blut.",
        "long_description": "Triglyzeride sind eine Art von Fett im Blut, das überschüssige Energie speichert..."
      }
    ],
    "interpretation": [
      {
        "title": "Gesamtgesundheitsbewertung",
        "shortcode": "overall_health_assessment",
        "subsections": [
          {
            "subtitle": "Umfassende Übersicht",
            "shortcode": "overall_health_assessment_overview",
            "items": [
              {"item": "Die Patientin zeigt allgemein gesunde hämatologische Parameter mit allen Blutbildwerten im Normalbereich."},
              {"item": "Die Ergebnisse des kompletten Blutbildes zeigen eine ausreichende Sauerstofftransportkapazität und Immunfunktion."},
              {"item": "Das Lipidprofil zeigt Bereiche, die Aufmerksamkeit erfordern, insbesondere die LDL-Cholesterinwerte."}
            ]
          }
        ]
      },
      {
        "title": "Stoffwechselanalyse",
        "shortcode": "metabolic_analysis",
        "subsections": [
          {
            "subtitle": "Blutzuckerbewertung",
            "shortcode": "metabolic_analysis_glucose",
            "items": [
              {"item": "Der Nüchternglukosewert von 102 mg/dL ist leicht erhöht und deutet auf einen Prädiabetes-Bereich hin."},
              {"item": "Es werden Lebensstiländerungen empfohlen, einschließlich regelmäßiger Bewegung und reduzierter Aufnahme einfacher Kohlenhydrate."},
              {"item": "Eine HbA1c-Nachuntersuchung wird in 3 Monaten empfohlen, um die langfristige Glukosekontrolle zu beurteilen."}
            ]
          }
        ]
      },
      {
        "title": "Kardiovaskuläre Risikobewertung",
        "shortcode": "cardiovascular_risk",
        "subsections": [
          {
            "subtitle": "Lipidprofilanalyse",
            "shortcode": "cardiovascular_risk_lipids",
            "items": [
              {"item": "LDL-Cholesterin mit 142 mg/dL überschreitet die optimalen Werte und erfordert eine Intervention."},
              {"item": "HDL-Cholesterin mit 58 mg/dL bietet moderaten kardiovaskulären Schutz."},
              {"item": "Triglyzeride leicht erhöht; Ernährungsumstellungen zur Reduzierung der gesättigten Fettsäuren werden empfohlen."},
              {"item": "Das Verhältnis von Gesamtcholesterin zu HDL von 3,76 deutet auf ein moderates kardiovaskuläres Risiko hin."}
            ]
          }
        ]
      },
      {
        "title": "Empfehlungen",
        "shortcode": "recommendations",
        "subsections": [
          {
            "subtitle": "Lebensstiländerungen",
            "shortcode": "recommendations_lifestyle",
            "items": [
              {"item": "Erhöhen Sie die aerobe körperliche Aktivität auf mindestens 150 Minuten pro Woche."},
              {"item": "Übernehmen Sie eine mediterrane Ernährung, reich an Gemüse, Obst und gesunden Fetten."},
              {"item": "Begrenzen Sie verarbeitete Lebensmittel, gesättigte Fette und zugesetzten Zucker."},
              {"item": "Erwägen Sie eine Beratung durch einen Ernährungsberater für personalisierte Ernährungsempfehlungen."}
            ]
          },
          {
            "subtitle": "Nachfolgeuntersuchungen",
            "shortcode": "recommendations_followup",
            "items": [
              {"item": "Wiederholung des Lipidprofils in 3 Monaten nach Umsetzung der Lebensstiländerungen."},
              {"item": "HbA1c-Test empfohlen, um die durchschnittlichen Blutzuckerwerte zu beurteilen."},
              {"item": "Jährliches umfassendes Stoffwechselpanel zur laufenden Überwachung."}
            ]
          }
        ]
      }
    ]
  },
  "api_version": "v11",
  "timestamp": "2025-12-16T14:32:18Z"
}

Produktionsendpunkt mit umfassender Gesundheitsscore-Berechnung und Krankheitsrisikoanalyse. Erwartet dieselbe Anfrage wie /api/v12/18-09-2026/analyze und ergänzt die Antwort um die unten aufgeführten Felder. Verbraucht 1 Credit pro Anfrage.

cURL-Beispiel

curl -X POST "https://app.aibloodtestinterpret.com/api/v12/health-score/analyze" \
  -F "username=IHR_BENUTZERNAME" \
  -F "password=IHR_PASSWORT" \
  -F "language=de" \
  -F "[email protected]"

Zusätzliche Antwortfelder

{
  "health_score": {
    "overall": 78,
    "optimal": 4,
    "normal": 12,
    "warning": 3,
    "critical": 1,
    "total_parameters": 20,
    "score_interpretation": "good",
    "recommendations": [
      "Consider increasing vitamin D intake",
      "Schedule follow-up for cholesterol levels"
    ]
  },
  "disease_risks": [
    {"name": "Cardiovascular Disease", "percentage": "18%", "severity": "low"},
    {"name": "Type 2 Diabetes", "percentage": "12%", "severity": "low"},
    {"name": "Metabolic Syndrome", "percentage": "25%", "severity": "moderate"}
  ]
}

Produktionsendpunkt mit umfassender Gesundheitsscore-Berechnung und Krankheitsrisikoanalyse.

Zusätzliche Antwortfelder

{
  "health_score": {
    "overall": 78,
    "optimal": 4,
    "normal": 12,
    "warning": 3,
    "critical": 1,
    "total_parameters": 20,
    "score_interpretation": "gut",
    "recommendations": [
      "Erwägen Sie eine Erhöhung der Vitamin-D-Aufnahme",
      "Planen Sie eine Nachuntersuchung für Cholesterinwerte"
    ]
  },
  "disease_risks": [
    {"name": "Herz-Kreislauf-Erkrankung", "percentage": "18%", "severity": "niedrig"},
    {"name": "Typ-2-Diabetes", "percentage": "12%", "severity": "niedrig"},
    {"name": "Metabolisches Syndrom", "percentage": "25%", "severity": "mäßig"}
  ]
}

Analysiert Bluttestparameter-Trends über mehrere Testdaten, um Muster zu identifizieren und Gesundheitserkenntnisse zu liefern.

Anfrageparameter

analysis_options Object

blood_tests Array-Struktur

*Entweder lab_date oder results_date ist für jeden Bluttest erforderlich.

blood_tests[].parameters Array-Struktur

cURL-Beispiel

curl -X POST "https://app.aibloodtestinterpret.com/api/v1/analytics/trends/analyze" \
  -H "Content-Type: application/json" \
  -d '{
    "username": "IHR_BENUTZERNAME",
    "password": "IHR_PASSWORT",
    "language": "de",
    "analysis_type": "comprehensive",
    "blood_tests": [
      {
        "lab_date": "2024-01-15",
        "parameters": [
          {"name": "Glucose", "short_name": "GLU", "value": 108, "unit": "mg/dL", "range_min": 70, "range_max": 100},
          {"name": "HbA1c", "short_name": "HbA1c", "value": 6.2, "unit": "%", "range_min": 4.0, "range_max": 5.6},
          {"name": "Cholesterol", "short_name": "CHOL", "value": 225, "unit": "mg/dL", "range_min": 0, "range_max": 200},
          {"name": "LDL Cholesterol", "short_name": "LDL", "value": 145, "unit": "mg/dL", "range_min": 0, "range_max": 100},
          {"name": "HDL Cholesterol", "short_name": "HDL", "value": 42, "unit": "mg/dL", "range_min": 40, "range_max": 60}
        ]
      },
      {
        "lab_date": "2024-05-10",
        "parameters": [
          {"name": "Glucose", "short_name": "GLU", "value": 102, "unit": "mg/dL", "range_min": 70, "range_max": 100},
          {"name": "HbA1c", "short_name": "HbA1c", "value": 5.9, "unit": "%", "range_min": 4.0, "range_max": 5.6},
          {"name": "Cholesterol", "short_name": "CHOL", "value": 205, "unit": "mg/dL", "range_min": 0, "range_max": 200},
          {"name": "LDL Cholesterol", "short_name": "LDL", "value": 128, "unit": "mg/dL", "range_min": 0, "range_max": 100},
          {"name": "HDL Cholesterol", "short_name": "HDL", "value": 48, "unit": "mg/dL", "range_min": 40, "range_max": 60}
        ]
      },
      {
        "lab_date": "2024-08-22",
        "parameters": [
          {"name": "Glucose", "short_name": "GLU", "value": 96, "unit": "mg/dL", "range_min": 70, "range_max": 100},
          {"name": "HbA1c", "short_name": "HbA1c", "value": 5.6, "unit": "%", "range_min": 4.0, "range_max": 5.6},
          {"name": "Cholesterol", "short_name": "CHOL", "value": 192, "unit": "mg/dL", "range_min": 0, "range_max": 200},
          {"name": "LDL Cholesterol", "short_name": "LDL", "value": 108, "unit": "mg/dL", "range_min": 0, "range_max": 100},
          {"name": "HDL Cholesterol", "short_name": "HDL", "value": 54, "unit": "mg/dL", "range_min": 40, "range_max": 60}
        ]
      },
      {
        "lab_date": "2024-12-18",
        "parameters": [
          {"name": "Glucose", "short_name": "GLU", "value": 89, "unit": "mg/dL", "range_min": 70, "range_max": 100},
          {"name": "HbA1c", "short_name": "HbA1c", "value": 5.3, "unit": "%", "range_min": 4.0, "range_max": 5.6},
          {"name": "Cholesterol", "short_name": "CHOL", "value": 178, "unit": "mg/dL", "range_min": 0, "range_max": 200},
          {"name": "LDL Cholesterol", "short_name": "LDL", "value": 92, "unit": "mg/dL", "range_min": 0, "range_max": 100},
          {"name": "HDL Cholesterol", "short_name": "HDL", "value": 58, "unit": "mg/dL", "range_min": 40, "range_max": 60}
        ]
      }
    ]
  }'

Python-Beispiel

import requests
from typing import List, Dict, Optional

def trends_analysieren(
    benutzername: str,
    passwort: str,
    bluttests: List[Dict],
    sprache: str = "de",
    analyse_typ: str = "comprehensive"
) -> Dict:
    """
    Analysiert Bluttesttrends über die Zeit.

    Args:
        benutzername: API-Benutzername
        passwort: API-Passwort
        bluttests: Liste von Bluttestobjekten mit lab_date und parameters
        sprache: Antwortsprachcode
        analyse_typ: comprehensive, quick oder focused

    Returns:
        dict: Trendanalyseergebnisse
    """
    url = "https://app.aibloodtestinterpret.com/api/v1/analytics/trends/analyze"

    nutzlast = {
        "username": benutzername,
        "password": passwort,
        "language": sprache,
        "analysis_type": analyse_typ,
        "blood_tests": bluttests
    }

    antwort = requests.post(url, json=nutzlast, timeout=120)
    antwort.raise_for_status()
    return antwort.json()

# Anwendungsbeispiel
if __name__ == "__main__":
    bluttests = [
        {
            "lab_date": "2025-01-15",
            "parameters": [
                {"short_name": "HGB", "result": 12.5, "unit": "g/dL"},
                {"short_name": "CHOL", "result": 220, "unit": "mg/dL"}
            ]
        },
        {
            "lab_date": "2025-06-15",
            "parameters": [
                {"short_name": "HGB", "result": 13.2, "unit": "g/dL"},
                {"short_name": "CHOL", "result": 195, "unit": "mg/dL"}
            ]
        },
        {
            "lab_date": "2025-12-01",
            "parameters": [
                {"short_name": "HGB", "result": 14.1, "unit": "g/dL"},
                {"short_name": "CHOL", "result": 180, "unit": "mg/dL"}
            ]
        }
    ]

    ergebnis = trends_analysieren(
        benutzername="ihr_benutzername",
        passwort="ihr_passwort",
        bluttests=bluttests
    )

    print(f"Gesamttrend: {ergebnis['data']['overall_trend']}")
    for trend in ergebnis['data']['parameter_trends']:
        print(f"{trend['parameter']}: {trend['direction']} ({trend['change_percent']}%)")

Antwortfelder-Referenz

parameter_trends Objektstruktur

Beispielantwort

{
  "api_version": "1.0.0",
  "status": "success",
  "message": "Trendanalyse erfolgreich abgeschlossen",
  "timestamp": "2025-12-22T01:12:49.262700Z",
  "data": {
    "analysis_id": "TRD-49B4C616",
    "analysis_period": {
      "start_date": "2024-01-15",
      "end_date": "2024-12-18",
      "span_months": 11,
      "total_tests": 4
    },
    "categories": [
      "Stoffwechsel-Panel",
      "Vitamine",
      "Diabetes-Marker",
      "Eisenstudien",
      "Lipid-Panel",
      "Großes Blutbild"
    ],
    "chart_config": {
      "dates": ["Jan 2024", "Mai 2024", "Sep 2024", "Dez 2024"],
      "raw_dates": ["2024-01-15", "2024-05-20", "2024-09-10", "2024-12-18"]
    },
    "language": "de",
    "overall_health_trend": {
      "summary": "Insgesamt zeigen die Bluttestparameter positive Trends mit Verbesserungen bei Hämoglobin, Lipidprofil einschließlich LDL- und HDL-Cholesterin, Vitamin-D-Status und Eisenspeichern.",
      "health_risks": [],
      "recommendations": []
    },
    "parameter_trends": [
      {
        "parameter": "Hämoglobin (Hb)",
        "short_name": "Hämoglobin",
        "category": "Großes Blutbild",
        "unit": "g/dL",
        "trend_data": [
          {"date": "2024-01-15", "value": 12.8},
          {"date": "2024-05-20", "value": 13.5},
          {"date": "2024-09-10", "value": 14.2},
          {"date": "2024-12-18", "value": 14.8}
        ],
        "statistical_analysis": {
          "average": 13.82,
          "min": 12.8,
          "max": 14.8,
          "standard_deviation": 0.87,
          "trend_direction": "aufwärts",
          "trend_strength": "moderat"
        },
        "analysis": {
          "description": "Hämoglobin misst das sauerstofftransportierende Protein in roten Blutkörperchen.",
          "significant_variations": "Anfänglich niedrig bei 12,8 g/dL, dann schrittweise auf 14,8 g/dL gestiegen.",
          "trend": "steigend"
        }
      },
      {
        "parameter": "LDL-Cholesterin (LDL-C)",
        "short_name": "LDL-Cholesterin",
        "category": "Lipid-Panel",
        "unit": "mg/dL",
        "trend_data": [
          {"date": "2024-01-15", "value": 110.0},
          {"date": "2024-05-20", "value": 102.0},
          {"date": "2024-09-10", "value": 92.0},
          {"date": "2024-12-18", "value": 85.0}
        ],
        "statistical_analysis": {
          "average": 97.25,
          "min": 85.0,
          "max": 110.0,
          "standard_deviation": 11.0,
          "trend_direction": "abwärts",
          "trend_strength": "stark"
        },
        "analysis": {
          "description": "LDL-C ist das 'schlechte' Cholesterin, das mit erhöhtem Herzkrankheitsrisiko verbunden ist.",
          "significant_variations": "LDL-C sank von hoch (110 mg/dL) auf normal (85 mg/dL).",
          "trend": "sinkend"
        }
      }
    ],
    "risk_factors": [],
    "sandbox_mode": false
  }
}

Erstellt umfassende Ernährungs- und Nahrungsergänzungsmittelempfehlungen basierend auf Bluttestparametern und Patientenprofil.

Anfrageparameter

Patientenobjekt-Schema

Patienten-Demografieobjekt mit gender (siehe Werte) und activity_level (siehe Werte).

{
  "age": 45,
  "gender": "male",
  "weight": 82,
  "height": 178,
  "activity_level": "moderate",
  "conditions": ["hypertension"],
  "medications": ["lisinopril"],
  "allergies": ["shellfish"]
}

Python-Beispiel

import requests
from typing import Dict, List, Optional

def ernaehrungsplan_erstellen(
    benutzername: str,
    passwort: str,
    patient: Dict,
    bluttest: Dict,
    gesundheitsziele: Optional[List[str]] = None,
    ernaehrungseinschraenkungen: Optional[List[str]] = None,
    sprache: str = "de"
) -> Dict:
    """
    Erstellt personalisierte Ernährungs- und Nahrungsergänzungsmittelempfehlungen.

    Args:
        benutzername: API-Benutzername
        passwort: API-Passwort
        patient: Patientendemografie und Gesundheitsinformationen
        bluttest: Bluttestparameter
        gesundheitsziele: Optionale Gesundheitsziele
        ernaehrungseinschraenkungen: Optionale Ernährungseinschränkungen
        sprache: Antwortsprache

    Returns:
        dict: Ernährungsplan mit Nahrungsergänzungsmitteln
    """
    url = "https://app.aibloodtestinterpret.com/api/v1/nutrition/diet-plan/analyze"

    nutzlast = {
        "username": benutzername,
        "password": passwort,
        "language": sprache,
        "patient": patient,
        "blood_test": bluttest
    }

    if gesundheitsziele:
        nutzlast["health_goals"] = gesundheitsziele
    if ernaehrungseinschraenkungen:
        nutzlast["dietary_restrictions"] = ernaehrungseinschraenkungen

    antwort = requests.post(url, json=nutzlast, timeout=120)
    antwort.raise_for_status()
    return antwort.json()

# Anwendungsbeispiel
if __name__ == "__main__":
    patient = {
        "age": 45,
        "gender": "male",
        "weight": 82,
        "height": 178,
        "activity_level": "moderate",
        "conditions": ["hypertension"],
        "allergies": ["shellfish"]
    }

    bluttest = {
        "lab_date": "2025-12-01",
        "parameters": [
            {"short_name": "VITD", "result": 18, "unit": "ng/mL"},
            {"short_name": "FER", "result": 25, "unit": "ng/mL"},
            {"short_name": "CHOL", "result": 210, "unit": "mg/dL"}
        ]
    }

    ergebnis = ernaehrungsplan_erstellen(
        benutzername="ihr_benutzername",
        passwort="ihr_passwort",
        patient=patient,
        bluttest=bluttest,
        gesundheitsziele=["lower_cholesterol", "increase_energy"]
    )

    # Nahrungsergänzungsmittelempfehlungen ausgeben
    for supp in ergebnis['data']['nutrition_plan']['supplement_recommendations']:
        print(f"{supp['name']}: {supp['dosage']} - {supp['reason']}")

Beispielantwort

{
  "status": "success",
  "data": {
    "analysis_id": "NUT-1A2B3C4D",
    "generated_at": "2026-11-04T10:30:00Z",
    "patient_summary": {
      "age": 34,
      "gender": "female",
      "weight": 62,
      "height": 165,
      "conditions": [],
      "dietary_preferences": "omnivore"
    },
    "nutrition_plan": {
      "nutrition_summary": {
        "daily_calories": 2046,
        "macro_distribution": {
          "protein_g": 153,
          "fat_g": 57,
          "carbs_g": 230,
          "fiber_g": 29
        },
        "key_focus_areas": [
          "Rebuild iron stores (low ferritin and haemoglobin)",
          "Raise vitamin D into the optimal range",
          "Lower LDL cholesterol with fibre and unsaturated fats"
        ],
        "primary_recommendations": [
          "Eat a heme-iron source (red meat, poultry, fish or shellfish) at least once a day",
          "Pair plant iron with vitamin C and keep tea and coffee one hour away from meals",
          "Have oily fish 2-3 times a week and oats, legumes or psyllium every day"
        ]
      },
      "weekly_meal_plan": {
        "monday": {
          "breakfast": {
            "main_dish": "Spinach and mushroom omelette (2 eggs) on wholegrain toast",
            "sides": ["Orange segments", "Cherry tomatoes"],
            "calories": 510,
            "protein": 30,
            "preparation_time": "15 minutes",
            "cost_estimate": "$3-4"
          },
          "morning_snack": {
            "items": ["Pumpkin seeds (30 g)", "Kiwi"],
            "calories": 150
          },
          "lunch": {
            "main_dish": "Lentil and vegetable soup",
            "sides": ["Wholegrain bread", "Red pepper salad with lemon"],
            "calories": 640,
            "protein": 32,
            "preparation_time": "25 minutes",
            "cost_estimate": "$3-5"
          },
          "afternoon_snack": {
            "items": ["Greek yogurt (150 g)", "Strawberries"],
            "calories": 150
          },
          "dinner": {
            "main_dish": "Grilled lean beef steak (150 g)",
            "sides": ["Roasted sweet potato", "Steamed broccoli with lemon"],
            "calories": 600,
            "protein": 45,
            "preparation_time": "30 minutes",
            "cost_estimate": "$6-8"
          }
        }
        // "tuesday" ... "sunday": the same meals as "monday"
      },
      "food_recommendations": {
        "power_foods": [
          {
            "name": "Lean red meat (beef, lamb)",
            "benefits": "Heme iron is absorbed two to three times better than plant iron and refills ferritin fastest; also supplies B12 and zinc.",
            "serving_size": "120-150 g",
            "frequency": "3 times a week",
            "blood_marker_impact": "Ferritin, Hemoglobin"
          },
          {
            "name": "Mussels and clams",
            "benefits": "Among the richest heme-iron foods, with B12 and omega-3 fats.",
            "serving_size": "100 g (cooked, shelled)",
            "frequency": "Once a week",
            "blood_marker_impact": "Ferritin, Hemoglobin"
          }
          // 12-15 foods in a live plan
        ],
        "foods_to_limit": [
          {
            "name": "Tea and coffee with meals",
            "reason": "Tannins and polyphenols can cut iron absorption from a meal by more than half.",
            "alternative": "Drink them at least one hour before or after eating; water or rooibos with meals",
            "impact_on_health": "Slows the rebuilding of ferritin"
          },
          {
            "name": "Processed meats (sausages, salami, bacon)",
            "reason": "High in saturated fat and salt, which raise LDL and blood pressure.",
            "alternative": "Grilled chicken, turkey or lean beef",
            "impact_on_health": "Keeps LDL cholesterol high"
          }
          // 12-15 foods in a live plan
        ],
        "hydration": {
          "daily_water": "3.1 L",
          "timing": "A glass on waking, then evenly through the day; less in the hour before bed",
          "special_considerations": "Keep tea and coffee at least one hour away from iron-rich meals"
        }
      },
      "supplement_recommendations": [
        {
          "name": "Iron bisglycinate",
          "dosage": "25 mg elemental iron a day",
          "timing": "In the morning on an empty stomach, with vitamin C",
          "reason": "Ferritin 14 ng/mL and haemoglobin 11.8 g/dL show depleted iron stores",
          "duration": "3 months, then retest ferritin",
          "contraindications": "Haemochromatosis; take 2 hours apart from calcium, antacids and thyroid medication",
          "source": "evidence_based",
          "priority": "high",
          "blood_marker_addressed": "Ferritin, Hemoglobin",
          "priority_label": "High",
          "source_label": "Evidence-Based"
        },
        {
          "name": "Vitamin D3",
          "dosage": "2000 IU a day",
          "timing": "With the largest meal of the day",
          "reason": "Vitamin D 18 ng/mL is below the 30 ng/mL threshold",
          "duration": "3 months, then retest",
          "contraindications": "Hypercalcaemia, sarcoidosis",
          "source": "evidence_based",
          "priority": "high",
          "blood_marker_addressed": "Vitamin D (25-OH)",
          "priority_label": "High",
          "source_label": "Evidence-Based"
        }
        // 5-8 supplements in a live plan
      ],
      "lifestyle_integration": {
        "meal_prep_tips": [
          "Cook a large pot of lentils or chickpeas on Sunday for three lunches",
          "Keep cut peppers and kiwi ready to add vitamin C to meals",
          "Portion nuts and seeds into small boxes for snacks"
        ],
        "shopping_list": {
          "proteins": ["Lean beef", "Salmon", "Sardines", "Mussels", "Chicken liver", "Eggs"],
          "vegetables": ["Spinach", "Kale", "Broccoli", "Bell peppers", "Tomatoes"],
          "fruits": ["Kiwi", "Oranges", "Strawberries", "Dried apricots"],
          "grains": ["Oats", "Quinoa", "Brown rice", "Wholegrain bread"],
          "dairy_alternatives": ["Greek yogurt", "Kefir", "Cottage cheese"],
          "pantry_staples": ["Lentils", "Chickpeas", "Pumpkin seeds", "Olive oil", "Psyllium husk"]
        },
        "eating_schedule": {
          "breakfast": "07:30-08:30",
          "morning_snack": "10:30",
          "lunch": "12:30-13:30",
          "afternoon_snack": "16:00",
          "dinner": "19:00-20:00"
        },
        "special_occasions": "When eating out, choose grilled meat or fish with salad, and keep coffee for after the meal."
      },
      "monitoring_plan": {
        "weekly_goals": [
          "Heme iron at least 5 days a week",
          "Oily fish twice a week",
          "No tea or coffee within an hour of meals"
        ],
        "tracking_metrics": [
          "Energy levels",
          "Fatigue and breathlessness",
          "Weight",
          "Digestive tolerance of the iron supplement"
        ],
        "adjustment_signs": [
          "Constipation or stomach upset from the iron supplement",
          "No improvement in energy after 6 weeks"
        ],
        "follow_up_tests": [
          "Ferritin, iron and haemoglobin in 8-12 weeks",
          "Vitamin D (25-OH) in 3 months",
          "Lipid panel in 3 months"
        ],
        "expected_improvements": [
          "Ferritin up 15-30 ng/mL within 3 months",
          "Vitamin D above 30 ng/mL within 3 months",
          "LDL down 10-15% within 3 months"
        ]
      },
      "educational_insights": {
        "blood_test_connections": [
          {
            "parameter": "Ferritin",
            "current_value": "14 ng/mL",
            "dietary_impact": "Iron stores are low; heme iron and vitamin C with meals refill them fastest.",
            "foods_that_help": ["Lean red meat", "Mussels", "Chicken liver", "Lentils with peppers"]
          },
          {
            "parameter": "Hemoglobin",
            "current_value": "11.8 g/dL",
            "dietary_impact": "Slightly below range, consistent with iron deficiency; it rises as ferritin recovers.",
            "foods_that_help": ["Beef", "Spinach with lemon", "Eggs"]
          }
          // one entry per relevant blood test parameter
        ],
        "nutritional_science": [
          "Heme iron from animal foods is absorbed at 15-35%, plant iron at 2-20%.",
          "Vitamin C turns plant iron into a form the gut absorbs more easily.",
          "Beta-glucan from oats forms a gel that carries cholesterol-rich bile out of the body."
        ],
        "success_strategies": [
          "Plan the week's meals and shop once",
          "Add a vitamin C food to every iron-rich meal",
          "Retest on schedule to see the progress"
        ]
      },
      "metadata": {
        "generated_at": "2026-11-04T10:30:00",
        "patient_id": "api_1a2b3c4d",
        "bmr": 1320.25,
        "tdee": 2046.39,
        "nutritional_concerns": ["vitamin_d_deficiency", "anemia", "iron_deficiency", "elevated_ldl"],
        "version": "2.2",
        "used_clinic_supplements": false,
        "clinic_supplements_count": 0,
        "report_language": "english",
        "meal_slots": ["breakfast", "morning_snack", "lunch", "afternoon_snack", "dinner"],
        "meal_frequency": "3 meals and 2 snacks a day (breakfast, morning snack, lunch, afternoon snack, dinner)",
        "budget": "moderate",
        "api_provider": "KantestiAI Engine",
        "model": "KantestiAI Engine"
      }
    },
    "labels": {
      "nutrition_summary": "Nutrition Summary",
      "key_focus_areas": "Key Focus Areas",
      "weekly_meal_plan": "Weekly Meal Plan",
      "monday": "Monday",
      "breakfast": "Breakfast",
      "morning_snack": "Morning Snack",
      "main_dish": "Main Dish",
      "supplement_recommendations": "Supplement Recommendations",
      "priority": "Priority",
      "high": "High",
      "evidence_based": "Evidence-Based"
      // ... one entry for every key of nutrition_plan and every priority / source code
    },
    "language": "en",
    "sandbox_mode": false
  },
  "timestamp": "2026-11-04T10:30:00Z",
  "api_version": "1.0.0",
  "message": "Nutrition analysis completed successfully"
}

Antwortfelder-Referenz

Der Produktionsendpunkt (/analyze) und die Sandbox liefern dieselbe Struktur. JSON-Schlüssel und Codes sind immer auf Englisch; alle Texte sind in der mit language angeforderten Sprache (Sandbox-Texte sind immer auf Englisch). Zeigen Sie Benutzern keine Schlüssel oder Codes an: Entnehmen Sie die Titel aus labels und zeigen Sie für Priorität und Quelle eines Nahrungsergänzungsmittels priority_label und source_label an. Jeder Tag in weekly_meal_plan, von monday bis sunday, hat dieselben Mahlzeiten: die von meal_frequency, standardmäßig fünf.

Der Plan wird in 39 Sprachen verfasst: en, de, fr, it, es, pt, ar, he, el, pl, tr, zh, ru, fa, ja, ko, nl, hi, id, th, vi, sv, no, da, fi, cs, ms, af, uk, ro, hu, sk, sl, hr, bg, sr, lv, lt, et. Jeder andere unterstützte Sprachcode (language) liefert den Plan und seine labels auf Englisch.

data Objekt

nutrition_plan Objekt

nutrition_plan.nutrition_summary Objekt

weekly_meal_plan.<day> Objekt

Ein Tag hat genau die Mahlzeiten von meal_frequency, und metadata.meal_slots listet sie auf: bei 2 nur lunch und dinner. Zeigen Sie die Mahlzeiten an, die ein Tag hat, nicht eine feste Liste von fünf.

breakfast / lunch / dinner Objekt

food_recommendations.power_foods Array-Element

food_recommendations.foods_to_limit Array-Element

supplement_recommendations Array-Element

Vollständiges cURL-Beispiel mit Antwort

curl -X POST "https://app.aibloodtestinterpret.com/api/v1/nutrition/diet-plan/analyze" \
  -H "Content-Type: application/json" \
  -d '{
    "username": "demo_user",
    "password": "demo_pass",
    "language": "de",
    "patient": {
      "age": 42,
      "gender": "female",
      "weight": 68,
      "height": 165,
      "activity_level": "moderate",
      "conditions": ["iron_deficiency_anemia"],
      "allergies": ["gluten"],
      "dietary_preferences": ["mediterranean"],
      "dietary_restrictions": ["gluten_free"],
      "liked_foods": ["salmon", "spinach", "quinoa"],
      "disliked_foods": ["liver"],
      "meal_frequency": 5,
      "budget": "moderate",
      "medications": ["ferrous_sulfate"]
    },
    "blood_test": {
      "lab_date": "2025-12-20",
      "parameters": [
        {"short_name": "FER", "result": 12, "unit": "ng/mL"},
        {"short_name": "HGB", "result": 10.8, "unit": "g/dL"},
        {"short_name": "VITD", "result": 22, "unit": "ng/mL"}
      ]
    },
    "health_goals": ["increase_energy", "improve_iron_levels"]
  }'

Antwort:

{
  "status": "success",
  "data": {
    "analysis_id": "NUT-1A2B3C4D",
    "generated_at": "2026-11-04T10:30:00Z",
    "patient_summary": {
      "age": 34,
      "gender": "female",
      "weight": 62,
      "height": 165,
      "conditions": [],
      "dietary_preferences": "omnivore"
    },
    "nutrition_plan": {
      "nutrition_summary": {
        "daily_calories": 2046,
        "macro_distribution": {
          "protein_g": 153,
          "fat_g": 57,
          "carbs_g": 230,
          "fiber_g": 29
        },
        "key_focus_areas": [
          "Rebuild iron stores (low ferritin and haemoglobin)",
          "Raise vitamin D into the optimal range",
          "Lower LDL cholesterol with fibre and unsaturated fats"
        ],
        "primary_recommendations": [
          "Eat a heme-iron source (red meat, poultry, fish or shellfish) at least once a day",
          "Pair plant iron with vitamin C and keep tea and coffee one hour away from meals",
          "Have oily fish 2-3 times a week and oats, legumes or psyllium every day"
        ]
      },
      "weekly_meal_plan": {
        "monday": {
          "breakfast": {
            "main_dish": "Spinach and mushroom omelette (2 eggs) on wholegrain toast",
            "sides": ["Orange segments", "Cherry tomatoes"],
            "calories": 510,
            "protein": 30,
            "preparation_time": "15 minutes",
            "cost_estimate": "$3-4"
          },
          "morning_snack": {
            "items": ["Pumpkin seeds (30 g)", "Kiwi"],
            "calories": 150
          },
          "lunch": {
            "main_dish": "Lentil and vegetable soup",
            "sides": ["Wholegrain bread", "Red pepper salad with lemon"],
            "calories": 640,
            "protein": 32,
            "preparation_time": "25 minutes",
            "cost_estimate": "$3-5"
          },
          "afternoon_snack": {
            "items": ["Greek yogurt (150 g)", "Strawberries"],
            "calories": 150
          },
          "dinner": {
            "main_dish": "Grilled lean beef steak (150 g)",
            "sides": ["Roasted sweet potato", "Steamed broccoli with lemon"],
            "calories": 600,
            "protein": 45,
            "preparation_time": "30 minutes",
            "cost_estimate": "$6-8"
          }
        }
        // "tuesday" ... "sunday": the same meals as "monday"
      },
      "food_recommendations": {
        "power_foods": [
          {
            "name": "Lean red meat (beef, lamb)",
            "benefits": "Heme iron is absorbed two to three times better than plant iron and refills ferritin fastest; also supplies B12 and zinc.",
            "serving_size": "120-150 g",
            "frequency": "3 times a week",
            "blood_marker_impact": "Ferritin, Hemoglobin"
          },
          {
            "name": "Mussels and clams",
            "benefits": "Among the richest heme-iron foods, with B12 and omega-3 fats.",
            "serving_size": "100 g (cooked, shelled)",
            "frequency": "Once a week",
            "blood_marker_impact": "Ferritin, Hemoglobin"
          }
          // 12-15 foods in a live plan
        ],
        "foods_to_limit": [
          {
            "name": "Tea and coffee with meals",
            "reason": "Tannins and polyphenols can cut iron absorption from a meal by more than half.",
            "alternative": "Drink them at least one hour before or after eating; water or rooibos with meals",
            "impact_on_health": "Slows the rebuilding of ferritin"
          },
          {
            "name": "Processed meats (sausages, salami, bacon)",
            "reason": "High in saturated fat and salt, which raise LDL and blood pressure.",
            "alternative": "Grilled chicken, turkey or lean beef",
            "impact_on_health": "Keeps LDL cholesterol high"
          }
          // 12-15 foods in a live plan
        ],
        "hydration": {
          "daily_water": "3.1 L",
          "timing": "A glass on waking, then evenly through the day; less in the hour before bed",
          "special_considerations": "Keep tea and coffee at least one hour away from iron-rich meals"
        }
      },
      "supplement_recommendations": [
        {
          "name": "Iron bisglycinate",
          "dosage": "25 mg elemental iron a day",
          "timing": "In the morning on an empty stomach, with vitamin C",
          "reason": "Ferritin 14 ng/mL and haemoglobin 11.8 g/dL show depleted iron stores",
          "duration": "3 months, then retest ferritin",
          "contraindications": "Haemochromatosis; take 2 hours apart from calcium, antacids and thyroid medication",
          "source": "evidence_based",
          "priority": "high",
          "blood_marker_addressed": "Ferritin, Hemoglobin",
          "priority_label": "High",
          "source_label": "Evidence-Based"
        },
        {
          "name": "Vitamin D3",
          "dosage": "2000 IU a day",
          "timing": "With the largest meal of the day",
          "reason": "Vitamin D 18 ng/mL is below the 30 ng/mL threshold",
          "duration": "3 months, then retest",
          "contraindications": "Hypercalcaemia, sarcoidosis",
          "source": "evidence_based",
          "priority": "high",
          "blood_marker_addressed": "Vitamin D (25-OH)",
          "priority_label": "High",
          "source_label": "Evidence-Based"
        }
        // 5-8 supplements in a live plan
      ],
      "lifestyle_integration": {
        "meal_prep_tips": [
          "Cook a large pot of lentils or chickpeas on Sunday for three lunches",
          "Keep cut peppers and kiwi ready to add vitamin C to meals",
          "Portion nuts and seeds into small boxes for snacks"
        ],
        "shopping_list": {
          "proteins": ["Lean beef", "Salmon", "Sardines", "Mussels", "Chicken liver", "Eggs"],
          "vegetables": ["Spinach", "Kale", "Broccoli", "Bell peppers", "Tomatoes"],
          "fruits": ["Kiwi", "Oranges", "Strawberries", "Dried apricots"],
          "grains": ["Oats", "Quinoa", "Brown rice", "Wholegrain bread"],
          "dairy_alternatives": ["Greek yogurt", "Kefir", "Cottage cheese"],
          "pantry_staples": ["Lentils", "Chickpeas", "Pumpkin seeds", "Olive oil", "Psyllium husk"]
        },
        "eating_schedule": {
          "breakfast": "07:30-08:30",
          "morning_snack": "10:30",
          "lunch": "12:30-13:30",
          "afternoon_snack": "16:00",
          "dinner": "19:00-20:00"
        },
        "special_occasions": "When eating out, choose grilled meat or fish with salad, and keep coffee for after the meal."
      },
      "monitoring_plan": {
        "weekly_goals": [
          "Heme iron at least 5 days a week",
          "Oily fish twice a week",
          "No tea or coffee within an hour of meals"
        ],
        "tracking_metrics": [
          "Energy levels",
          "Fatigue and breathlessness",
          "Weight",
          "Digestive tolerance of the iron supplement"
        ],
        "adjustment_signs": [
          "Constipation or stomach upset from the iron supplement",
          "No improvement in energy after 6 weeks"
        ],
        "follow_up_tests": [
          "Ferritin, iron and haemoglobin in 8-12 weeks",
          "Vitamin D (25-OH) in 3 months",
          "Lipid panel in 3 months"
        ],
        "expected_improvements": [
          "Ferritin up 15-30 ng/mL within 3 months",
          "Vitamin D above 30 ng/mL within 3 months",
          "LDL down 10-15% within 3 months"
        ]
      },
      "educational_insights": {
        "blood_test_connections": [
          {
            "parameter": "Ferritin",
            "current_value": "14 ng/mL",
            "dietary_impact": "Iron stores are low; heme iron and vitamin C with meals refill them fastest.",
            "foods_that_help": ["Lean red meat", "Mussels", "Chicken liver", "Lentils with peppers"]
          },
          {
            "parameter": "Hemoglobin",
            "current_value": "11.8 g/dL",
            "dietary_impact": "Slightly below range, consistent with iron deficiency; it rises as ferritin recovers.",
            "foods_that_help": ["Beef", "Spinach with lemon", "Eggs"]
          }
          // one entry per relevant blood test parameter
        ],
        "nutritional_science": [
          "Heme iron from animal foods is absorbed at 15-35%, plant iron at 2-20%.",
          "Vitamin C turns plant iron into a form the gut absorbs more easily.",
          "Beta-glucan from oats forms a gel that carries cholesterol-rich bile out of the body."
        ],
        "success_strategies": [
          "Plan the week's meals and shop once",
          "Add a vitamin C food to every iron-rich meal",
          "Retest on schedule to see the progress"
        ]
      },
      "metadata": {
        "generated_at": "2026-11-04T10:30:00",
        "patient_id": "api_1a2b3c4d",
        "bmr": 1320.25,
        "tdee": 2046.39,
        "nutritional_concerns": ["vitamin_d_deficiency", "anemia", "iron_deficiency", "elevated_ldl"],
        "version": "2.2",
        "used_clinic_supplements": false,
        "clinic_supplements_count": 0,
        "report_language": "english",
        "meal_slots": ["breakfast", "morning_snack", "lunch", "afternoon_snack", "dinner"],
        "meal_frequency": "3 meals and 2 snacks a day (breakfast, morning snack, lunch, afternoon snack, dinner)",
        "budget": "moderate",
        "api_provider": "KantestiAI Engine",
        "model": "KantestiAI Engine"
      }
    },
    "labels": {
      "nutrition_summary": "Nutrition Summary",
      "key_focus_areas": "Key Focus Areas",
      "weekly_meal_plan": "Weekly Meal Plan",
      "monday": "Monday",
      "breakfast": "Breakfast",
      "morning_snack": "Morning Snack",
      "main_dish": "Main Dish",
      "supplement_recommendations": "Supplement Recommendations",
      "priority": "Priority",
      "high": "High",
      "evidence_based": "Evidence-Based"
      // ... one entry for every key of nutrition_plan and every priority / source code
    },
    "language": "en",
    "sandbox_mode": false
  },
  "timestamp": "2026-11-04T10:30:00Z",
  "api_version": "1.0.0",
  "message": "Nutrition analysis completed successfully"
}

Analysiert 2-20 Bluttests und liefert einen detaillierten Vergleich mit KI-generierten narrativen Erkenntnissen.

Anfrageparameter

blood_tests Array-Struktur

*Entweder lab_date oder results_date ist für jeden Bluttest erforderlich.

Python-Beispiel

import requests
from typing import Dict, List

def bluttests_vergleichen(
    benutzername: str,
    passwort: str,
    bluttests: List[Dict],
    sprache: str = "de"
) -> Dict:
    """
    Vergleicht mehrere Bluttests für Trendanalyse.

    Args:
        benutzername: API-Benutzername
        passwort: API-Passwort
        bluttests: Liste von Bluttestobjekten (2-20 Tests)
        sprache: Antwortsprache

    Returns:
        dict: Vergleichsergebnisse mit KI-narrativen Erkenntnissen
    """
    url = "https://app.aibloodtestinterpret.com/api/v1/bloodtest/comparison/analyze"

    if len(bluttests) < 2:
        raise ValueError("Mindestens 2 Bluttests erforderlich")
    if len(bluttests) > 20:
        raise ValueError("Maximal 20 Bluttests erlaubt")

    nutzlast = {
        "username": benutzername,
        "password": passwort,
        "language": sprache,
        "blood_tests": bluttests
    }

    antwort = requests.post(url, json=nutzlast, timeout=120)
    antwort.raise_for_status()
    return antwort.json()

# Anwendungsbeispiel
if __name__ == "__main__":
    tests = [
        {
            "lab_date": "2024-06-15",
            "parameters": [
                {"short_name": "HGB", "result": 12.2, "unit": "g/dL"},
                {"short_name": "CHOL", "result": 235, "unit": "mg/dL"},
                {"short_name": "LDL", "result": 155, "unit": "mg/dL"}
            ]
        },
        {
            "lab_date": "2024-12-15",
            "parameters": [
                {"short_name": "HGB", "result": 14.5, "unit": "g/dL"},
                {"short_name": "CHOL", "result": 185, "unit": "mg/dL"},
                {"short_name": "LDL", "result": 98, "unit": "mg/dL"}
            ]
        }
    ]

    ergebnis = bluttests_vergleichen("ihr_benutzername", "ihr_passwort", tests)

    print(f"Gesamttrend: {ergebnis['data']['comparison_summary']['overall_trend']}")
    for param in ergebnis['data']['parameter_analysis']:
        print(f"{param['parameter_name']}: {param['trend_assessment']}")

Antwortfelder-Referenz

parameter_analysis Objektstruktur

Beispielantwort

{
  "api_version": "1.0.0",
  "status": "success",
  "message": "Bluttestvergleich erfolgreich abgeschlossen",
  "timestamp": "2025-12-22T01:12:43.057537Z",
  "data": {
    "comparison_id": "CMP-F4ACEE52",
    "tests_compared": 2,
    "date_range": {
      "earliest": "2024-06-15",
      "latest": "2024-12-15",
      "span_days": 183
    },
    "comparison_summary": {
      "overall_trend": "verbessert",
      "report1_date": "2024-06-15",
      "report2_date": "2024-12-15",
      "time_interval": "183 Tage zwischen Berichten",
      "key_findings": [
        "Hämoglobin- und RBC-Werte normalisiert, was auf Behebung der Anämie hinweist",
        "Glukose und HbA1c verbessert auf Normalbereich",
        "Lipidprofil verbessert mit normalisierten Cholesterinwerten"
      ]
    },
    "parameter_analysis": [
      {
        "parameter_name": "Hämoglobin",
        "report1_value": "12,2 g/dL",
        "report2_value": "14,5 g/dL",
        "change_type": "increased",
        "change_magnitude": "significant",
        "clinical_significance": "Verbesserung von Anämie zu normalen Hämoglobinwerten",
        "trend_assessment": "positive"
      },
      {
        "parameter_name": "LDL-Cholesterin",
        "report1_value": "155 mg/dL",
        "report2_value": "98 mg/dL",
        "change_type": "decreased",
        "change_magnitude": "significant",
        "clinical_significance": "LDL nahe optimalem Bereich, reduziert Atheroskleroserisiko",
        "trend_assessment": "positive"
      }
    ],
    "health_assessment": {
      "overall_health_trend": "verbessert",
      "areas_of_improvement": [
        "Anämiekorrektur",
        "Glykämische Kontrolle",
        "Lipidprofil-Normalisierung"
      ],
      "areas_of_concern": [],
      "positive_developments": [
        "Behebung der Anämie",
        "Normale Glukose und HbA1c",
        "Verbessertes kardiovaskuläres Risikoprofil"
      ]
    },
    "recommendations": {
      "immediate_actions": [
        "Aktuelle Supplementierung für Eisen und Vitamin D fortsetzen"
      ],
      "follow_up_tests": [
        "Blutbild und Eisenstudien in 3 Monaten wiederholen",
        "Nüchternglukose und HbA1c vierteljährlich überwachen"
      ],
      "lifestyle_modifications": [
        "Herzgesunde Ernährung mit wenig gesättigten Fetten"
      ],
      "monitoring_frequency": "3 Monate"
    },
    "summary": {
      "improved_parameters": 13,
      "stable_parameters": 0,
      "worsened_parameters": 0,
      "overall_trend": "verbessert"
    },
    "sandbox_mode": false
  }
}

Gibt API-Plattforminformationen, verfügbare Versionen und unterstützte Funktionen zurück. Keine Authentifizierung erforderlich.

Beispielantwort

{
  "platform": "Kantesti Bluttest-Analyse-API",
  "versions": ["v6", "v8", "v9", "v10", "v11"],
  "latest_version": "v11",
  "supported_languages": 100,
  "documentation": "https://www.kantesti.net/docs/",
  "status": "operational"
}

Gesundheitsprüfungs-Endpunkt für Überwachung. Gibt Servicestatus zurück. Keine Authentifizierung erforderlich.

Beispielantwort

{
  "status": "healthy",
  "timestamp": "2025-12-22T10:30:00Z",
  "uptime": "99.99%"
}

Überprüfen Sie Ihr verbleibendes API-Kontingent. Erfordert Authentifizierung.

cURL-Beispiel

curl -X POST "https://app.aibloodtestinterpret.com/api/quota/check" \
  -H "Content-Type: application/json" \
  -d '{"username": "IHR_BENUTZERNAME", "password": "IHR_PASSWORT"}'

Beispielantwort

{
  "status": "success",
  "quota": {
    "remaining": 847,
    "total": 1000,
    "reset_date": "2026-01-01",
    "plan": "professional"
  }
}

Erstellen Sie einen umfassenden KI-gestützten Familiengesundheits-Risikobewertungsbericht. Analysiert die familiäre Krankengeschichte, Gesundheitsprofile und Bluttestdaten zur Identifizierung erblicher Risikofaktoren mit personalisierten Vorsorgeempfehlungen.

Anfrageparameter (JSON Body)

patient_data Objekt

family_members Array-Element

cURL-Beispiel

curl -X POST "https://app.aibloodtestinterpret.com/api/v1/family-health/analyze" \
  -H "Content-Type: application/json" \
  -d '{
    "username": "IHR_BENUTZERNAME",
    "password": "IHR_PASSWORT",
    "patient_data": {
      "name": "Anna Müller",
      "age": 42,
      "gender": "female"
    },
    "family_members": [
      {"relation": "father", "age": 70, "conditions": ["hypertension", "type2_diabetes"]},
      {"relation": "mother", "age": 67, "conditions": ["breast_cancer"]}
    ],
    "language": "de"
  }'

Python-Beispiel

import requests

url = "https://app.aibloodtestinterpret.com/api/v1/family-health/analyze"
payload = {
    "username": "IHR_BENUTZERNAME",
    "password": "IHR_PASSWORT",
    "patient_data": {"name": "Anna Müller", "age": 42, "gender": "female"},
    "family_members": [
        {"relation": "father", "age": 70, "conditions": ["hypertension", "type2_diabetes"]},
        {"relation": "mother", "age": 67, "conditions": ["breast_cancer"]}
    ],
    "language": "de"
}
response = requests.post(url, json=payload, timeout=120)
print(response.json())

Beispielantwort

{
  "status": "success",
  "data": {
    "report_data": {
      "report_title": "Familiengesundheits-Risikobewertungsbericht",
      "executive_summary": "Basierend auf der Analyse der familiären Krankengeschichte...",
      "hereditary_risk_analysis": {
        "high_risk": [{"condition": "Herz-Kreislauf-Erkrankung", "risk_score": 75}],
        "moderate_risk": [{"condition": "Typ-2-Diabetes", "risk_score": 60}],
        "low_risk": []
      },
      "genetic_screening_recommendations": ["BRCA1/BRCA2 Gentest", "Kardiovaskuläres Genpanel"],
      "preventive_care_timeline": [
        {"age_range": "40-45", "screenings": ["Jährliche Mammographie", "Belastungs-EKG"]}
      ]
    }
  },
  "message": "Familiengesundheits-Risikobewertung erfolgreich abgeschlossen",
  "timestamp": "2026-03-23T10:30:00Z",
  "api_version": "1.0.0"
}

Family Health API Fehlercodes

Bildet jedes markierte Ergebnis eines Laborpanels auf den Körper ab. Verbraucht 1 Credit pro erfolgreicher Anfrage. Eine Anfrage, die die Validierung nicht besteht oder deren markierte Marker nicht zugeordnet werden können, wird nicht berechnet.

Anfrageparameter

Felder des Parameter-Objekts

cURL-Beispiel

curl -X POST "https://app.aibloodtestinterpret.com/api/v1/body-map/analyze" \
  -H "Content-Type: application/json" \
  -d '{
    "username": "IHR_BENUTZERNAME",
    "password": "IHR_PASSWORT",
    "parameters": [
      {"short_name": "ALT", "long_name": "Alanine aminotransferase", "result": "65", "unit": "U/L", "range_normal_min": 7, "range_normal_max": 45, "evaluation": "high"},
      {"short_name": "AST", "long_name": "Aspartate aminotransferase", "result": "48", "unit": "U/L", "range_normal_min": 8, "range_normal_max": 40, "evaluation": "slightly_high"},
      {"short_name": "TSH", "long_name": "Thyrotropin", "result": "6.2", "unit": "mIU/L", "range_normal_min": 0.4, "range_normal_max": 4.0, "evaluation": "high"}
    ]
  }'

Python-Beispiel

import requests

def koerperkarte_erstellen(parameter, benutzername, passwort, bild_einschliessen=False):
    """
    Auffällige Bluttestergebnisse auf den Körper abbilden.

    Args:
        parameter: Liste von Laborergebnis-Objekten
        benutzername: API-Benutzername
        passwort: API-Passwort
        bild_einschliessen: Gerenderte Illustration anfordern (verbraucht ein Bild-Credit)

    Returns:
        dict: Body-Map-Block mit Regionen, Legende und Illustrations-URLs
    """
    url = "https://app.aibloodtestinterpret.com/api/v1/body-map/analyze"

    antwort = requests.post(url, json={
        "username": benutzername,
        "password": passwort,
        "parameters": parameter,
        "include_image": bild_einschliessen,
    }, timeout=60)
    antwort.raise_for_status()
    return antwort.json()

# Anwendungsbeispiel
if __name__ == "__main__":
    ergebnis = koerperkarte_erstellen(
        parameter=[
            {"short_name": "ALT", "result": "65", "unit": "U/L",
             "range_normal_min": 7, "range_normal_max": 45, "evaluation": "high"},
            {"short_name": "TSH", "result": "6.2", "unit": "mIU/L",
             "range_normal_min": 0.4, "range_normal_max": 4.0, "evaluation": "high"},
        ],
        benutzername="ihr_benutzername",
        passwort="ihr_passwort",
    )

    body_map = ergebnis["data"]["body_map"]
    if ergebnis["data"]["all_clear"]:
        print("Alles unauffällig — nichts markiert.")
    for region in body_map["regions"]:
        schweregrad = "außerhalb des Referenzbereichs" if region["level"] == 2 else "grenzwertig"
        print(f"  {region['key']}: {schweregrad} ({', '.join(region['markers'])})")
    print(f"Illustration: {body_map['image_url'] or body_map['fallback_url']}")

Beispielantwort

{
  "status": "success",
  "api_version": "1.0.0",
  "message": "Body map generated successfully",
  "data": {
    "body_map": {
      "v": 4,
      "spec": "v4-thy2-liv2",
      "unmapped": 0,
      "image_url": "/static/body_maps/v4-thy2-liv2.webp",
      "fallback_url": "/body-map/v4-thy2-liv2.0123456789abcdef.webp",
      "regions": [
        {"key": "thyroid", "level": 2, "drawn": true, "markers": ["TSH"]},
        {"key": "liver", "level": 2, "drawn": true, "markers": ["ALT", "AST"]}
      ]
    },
    "engine_version": 4,
    "region_keys": ["brain_nerves", "thyroid", "heart_vessels", "liver", "pancreas", "adrenals", "kidneys", "gut", "reproductive", "blood", "immune", "bones", "muscles"],
    "mode": "deterministic",
    "all_clear": false
  },
  "timestamp": "2026-09-18T10:30:00Z"
}

Referenz der Antwortfelder

Prüft die Anfragedaten, ohne die Analyse auszuführen, und meldet, welche Ihrer Analytnamen die Engine erkennt. Authentifizierung ist erforderlich; es wird kein Kontingent verbraucht, und der Endpunkt funktioniert auch bei einem Konto ohne verbleibende Credits.

cURL-Beispiel

curl -X POST "https://app.aibloodtestinterpret.com/api/v1/body-map/validate" \
  -H "Content-Type: application/json" \
  -d '{
    "username": "IHR_BENUTZERNAME",
    "password": "IHR_PASSWORT",
    "parameters": [
      {"short_name": "ALT", "result": "65", "evaluation": "high"},
      {"short_name": "Unobtainium", "result": "9", "evaluation": "high"}
    ]
  }'

Beispielantwort

{
  "status": "success",
  "api_version": "1.0.0",
  "message": "Payload is valid",
  "data": {
    "valid": true,
    "errors": [],
    "parameter_count": 2,
    "flagged_count": 2,
    "recognised": [
      {"name": "ALT", "region": "liver", "level": 2}
    ],
    "unrecognised": [
      {"name": "Unobtainium", "region": null, "level": 2}
    ]
  },
  "timestamp": "2026-09-18T10:30:00Z"
}

Listet die 13 Körperregionen in kanonischer Reihenfolge zusammen mit den Schweregraden auf. Verwenden Sie ihn, um Ihre eigenen Legendenübersetzungen aufzubauen.

cURL-Beispiel

curl "https://app.aibloodtestinterpret.com/api/v1/body-map/regions"

Beispielantwort

{
  "status": "success",
  "data": {
    "regions": [
      {"key": "brain_nerves", "code": "brn", "order": 0},
      {"key": "thyroid", "code": "thy", "order": 1},
      {"key": "heart_vessels", "code": "hrt", "order": 2},
      {"key": "liver", "code": "liv", "order": 3}
    ],
    "region_keys": ["brain_nerves", "thyroid", "heart_vessels", "liver", "pancreas", "adrenals", "kidneys", "gut", "reproductive", "blood", "immune", "bones", "muscles"],
    "levels": {
      "0": "within range — not shown",
      "1": "borderline (slightly high / slightly low)",
      "2": "out of range (high / low / abnormal)"
    },
    "count": 13
  }
}

Metadaten zum Funktionsumfang: ob die Engine in dieser Installation aktiviert ist, die Anfragelimits, das Authentifizierungsverfahren und die vollständige Endpunktliste.

cURL-Beispiel

curl "https://app.aibloodtestinterpret.com/api/v1/body-map/info"

Berechnet das biologische Blutalter und die abgeleiteten Indizes aus einem Laborpanel. Verbraucht 1 Credit pro erfolgreicher Anfrage. Eine Anfrage, die die Validierung nicht besteht oder deren Panel nichts Berechenbares enthält, wird nicht berechnet.

Anfrageparameter

Die neun PhenoAge-Marker

Senden Sie sie unter den Namen, die Ihr Labor gedruckt hat — die Zuordnung erfolgt über den Namen in einer der 39 unterstützten Berichtssprachen, und Einheiten werden automatisch umgerechnet.

cURL-Beispiel

curl -X POST "https://app.aibloodtestinterpret.com/api/v1/blood-age/analyze" \
  -H "Content-Type: application/json" \
  -d '{
    "username": "IHR_BENUTZERNAME",
    "password": "IHR_PASSWORT",
    "metadata": {"patient_age": "40", "patient_sex": "Male", "lab_date": "2026-09-11"},
    "parameters": [
      {"short_name": "Albumin", "result": 4.4, "unit": "g/dL", "range_normal_min": 3.5, "range_normal_max": 5.0},
      {"short_name": "Creatinine", "result": 0.9, "unit": "mg/dL", "range_normal_min": 0.6, "range_normal_max": 1.2},
      {"short_name": "Glucose", "result": 90, "unit": "mg/dL", "range_normal_min": 70, "range_normal_max": 99},
      {"short_name": "CRP", "result": 1.0, "unit": "mg/L", "range_normal_min": 0, "range_normal_max": 5},
      {"short_name": "Lymphocytes", "result": 30, "unit": "%", "range_normal_min": 20, "range_normal_max": 40},
      {"short_name": "MCV", "result": 90, "unit": "fL", "range_normal_min": 80, "range_normal_max": 100},
      {"short_name": "RDW", "result": 13, "unit": "%", "range_normal_min": 11.5, "range_normal_max": 14.5},
      {"short_name": "ALP", "result": 70, "unit": "U/L", "range_normal_min": 40, "range_normal_max": 130},
      {"short_name": "WBC", "result": 6.0, "unit": "10^9/L", "range_normal_min": 4, "range_normal_max": 11}
    ]
  }'

Python-Beispiel

import requests

def biologisches_blutalter(parameter, metadaten, benutzername, passwort):
    """
    Biologisches Blutalter aus einem Routine-Blutpanel berechnen.

    Args:
        parameter: Liste von Laborergebnis-Objekten
        metadaten: Berichtskopf mit patient_age und patient_sex
        benutzername: API-Benutzername
        passwort: API-Passwort

    Returns:
        dict: Blutalter-Block, abgeleitete Indizes und eine flache Zusammenfassung
    """
    url = "https://app.aibloodtestinterpret.com/api/v1/blood-age/analyze"

    antwort = requests.post(url, json={
        "username": benutzername,
        "password": passwort,
        "parameters": parameter,
        "metadata": metadaten,
    }, timeout=60)
    antwort.raise_for_status()
    return antwort.json()

# Anwendungsbeispiel
if __name__ == "__main__":
    ergebnis = biologisches_blutalter(
        parameter=[
            {"short_name": "Albumin", "result": 4.4, "unit": "g/dL"},
            {"short_name": "Creatinine", "result": 0.9, "unit": "mg/dL"},
            {"short_name": "Glucose", "result": 90, "unit": "mg/dL"},
            {"short_name": "CRP", "result": 1.0, "unit": "mg/L"},
            {"short_name": "Lymphocytes", "result": 30, "unit": "%"},
            {"short_name": "MCV", "result": 90, "unit": "fL"},
            {"short_name": "RDW", "result": 13, "unit": "%"},
            {"short_name": "ALP", "result": 70, "unit": "U/L"},
            {"short_name": "WBC", "result": 6.0, "unit": "10^9/L"},
        ],
        metadaten={"patient_age": "40", "patient_sex": "Male"},
        benutzername="ihr_benutzername",
        passwort="ihr_passwort",
    )

    zusammenfassung = ergebnis["data"]["summary"]
    if zusammenfassung["status"] != "ok":
        print(f"Kein Alter berechnet: {zusammenfassung['status']}")
    else:
        print(f"Chronologisch: {zusammenfassung['chronological_age']}")
        print(f"Biologisch:    {zusammenfassung['biological_age']} ({zusammenfassung['source']})")
        print(f"Differenz:     {zusammenfassung['delta_years']:+} Jahre")

    for index in ergebnis["data"]["blood_age"]["indices"]:
        print(f"  {index['key']}: {index['value']} {index['unit']} [{index['band']}]")

Beispielantwort

{
  "status": "success",
  "api_version": "1.0.0",
  "message": "Biological blood age computed successfully",
  "data": {
    "blood_age": {
      "version": 1,
      "age": {
        "status": "ok",
        "source": "formula",
        "chrono": 40,
        "pheno": 35.1,
        "delta": -4.9,
        "sex": "m",
        "found": ["albumin", "creatinine", "glucose", "crp", "lymph", "mcv", "rdw", "alp", "wbc"],
        "missing": [],
        "labels": {"albumin": "Albumin", "creatinine": "Creatinine", "glucose": "Glucose"},
        "inputs": {"albumin": 44.0, "creatinine": 79.56, "glucose": 5.0}
      },
      "indices": [
        {"key": "fib4", "group": "liver", "value": 1.12, "unit": "", "band": "ok", "from": ["AST", "ALT", "PLT"]},
        {"key": "egfr", "group": "kidneys", "value": 98.0, "unit": "mL/min/1.73m2", "band": "ok", "from": ["Creatinine"]},
        {"key": "nlr", "group": "immune", "value": 1.8, "unit": "", "band": "ok", "from": ["Neutrophils", "Lymphocytes"]}
      ]
    },
    "engine_version": 1,
    "mode": "deterministic",
    "summary": {
      "status": "ok",
      "source": "formula",
      "chronological_age": 40,
      "biological_age": 35.1,
      "delta_years": -4.9,
      "sex": "m",
      "markers_found": 9,
      "markers_missing": [],
      "indices_count": 3
    }
  },
  "timestamp": "2026-09-18T10:30:00Z"
}

Referenz der Antwortfelder

Prüft die Anfragedaten, ohne die Analyse auszuführen, und meldet, welche der neun PhenoAge-Marker Ihr Panel liefert und ob ein chronologisches Alter gelesen werden konnte — die beiden Angaben, die darüber entscheiden, ob Sie die vollständige Formel oder die partielle Schätzung erhalten. Authentifizierung ist erforderlich; es wird kein Kontingent verbraucht, und der Endpunkt funktioniert auch bei einem Konto ohne verbleibende Credits.

cURL-Beispiel

curl -X POST "https://app.aibloodtestinterpret.com/api/v1/blood-age/validate" \
  -H "Content-Type: application/json" \
  -d '{
    "username": "IHR_BENUTZERNAME",
    "password": "IHR_PASSWORT",
    "metadata": {"patient_age": "40", "patient_sex": "Male"},
    "parameters": [
      {"short_name": "MCV", "result": 90, "unit": "fL"},
      {"short_name": "WBC", "result": 6.0, "unit": "10^9/L"},
      {"short_name": "Lymphocytes", "result": 30, "unit": "%"}
    ]
  }'

Beispielantwort

{
  "status": "success",
  "api_version": "1.0.0",
  "message": "Payload is valid",
  "data": {
    "valid": true,
    "errors": [],
    "parameter_count": 3,
    "looks_like_blood_panel": true,
    "chronological_age": 40,
    "sex": "m",
    "phenoage_markers_found": ["lymph", "mcv", "wbc"],
    "phenoage_markers_missing": ["albumin", "creatinine", "glucose", "crp", "rdw", "alp"],
    "expected_source": "partial",
    "recognised_markers": ["lymph_pct", "mcv", "wbc"]
  },
  "timestamp": "2026-09-18T10:30:00Z"
}

Listet die neun PhenoAge-Eingaben auf, jeden von der Engine lesbaren Marker mit seiner Zieleinheit sowie den Altersbereich, für den die Formel gilt.

cURL-Beispiel

curl "https://app.aibloodtestinterpret.com/api/v1/blood-age/biomarkers"

Beispielantwort

{
  "status": "success",
  "data": {
    "phenoage_inputs": ["albumin", "creatinine", "glucose", "crp", "lymph", "mcv", "rdw", "alp", "wbc"],
    "phenoage_age_range": {"min": 18, "max": 100},
    "markers": [
      {"key": "albumin", "target_unit": "g/l"},
      {"key": "alp", "target_unit": "u/l"},
      {"key": "alt", "target_unit": "u/l"}
    ],
    "marker_count": 33,
    "name_matching": "Markers are matched by the analyte name your laboratory printed, in any of the supported report languages. You never send these keys."
  }
}

Metadaten zum Funktionsumfang: ob die Engine in dieser Installation aktiviert ist, die beiden Modi und ihre jeweiligen Kosten, die Anfragelimits, das Authentifizierungsverfahren und die unterstützten Sprachen.

cURL-Beispiel

curl "https://app.aibloodtestinterpret.com/api/v1/blood-age/info"

Interpretiert einen DNA-Test und liefert einen umfassenden genetischen Gesundheitsbericht. Senden Sie eine Datei als multipart/form-data oder eingefügte Genotyp-Zeilen als JSON. Der Upload wird noch während der Anfrage eingelesen, sodass eine unlesbare Datei sofort mit 400 beantwortet wird und nichts kostet. Ein Credit wird erst berechnet, nachdem der Bericht erstellt wurde.

Anfrageparameter

cURL-Beispiel

curl -X POST "https://app.aibloodtestinterpret.com/api/v1/dna-interpretation/analyze?async=1" \
  -u "IHR_BENUTZERNAME:IHR_PASSWORT" \
  -F "file=@genome_raw_data.txt" \
  -F "language=en" \
  -F 'patient={"age": 41, "sex": "female", "medications": "clopidogrel"}'

# Antwort 202 Accepted: {"status": "pending", "job_id": "...", "poll_url": "/api/jobs/...", ...}
curl -u "IHR_BENUTZERNAME:IHR_PASSWORT" "https://app.aibloodtestinterpret.com/api/jobs/JOB_ID"

Python-Beispiel

import time
import requests

BASE = "https://app.aibloodtestinterpret.com"
AUTH = ("IHR_BENUTZERNAME", "IHR_PASSWORT")


def run(path, poll=True, **kwargs):
    """POST im asynchronen Modus, dann /api/jobs/<id> abfragen, bis der Bericht fertig ist."""
    resp = requests.post(f"{BASE}{path}?async=1", auth=AUTH, timeout=60, **kwargs)
    body = resp.json()
    if resp.status_code != 202:
        return body                      # ein Fehler oder eine synchrone Antwort
    while True:
        time.sleep(body.get("poll_interval_ms", 3000) / 1000)
        job = requests.get(f"{BASE}{body['poll_url']}", auth=AUTH, timeout=30).json()
        if job["status"] in ("completed", "failed"):
            return job["result"]["response"]


# 1) DNA-Test-Interpretation
with open("genome_raw_data.txt", "rb") as fh:
    dna = run("/api/v1/dna-interpretation/analyze",
              files={"file": fh},
              data={"language": "en", "patient": '{"age": 41, "sex": "female"}'})
report = dna["data"]["report"]
print(report["executive_summary"])
for section in report["sections"]:
    print(section["title"], "-", section["risk_level"])

Beispielantwort

{
  "status": "success",
  "api_version": "1.0.0",
  "message": "DNA test interpretation completed successfully",
  "data": {
    "analysis_id": "DNA-3F9A1C07B2",
    "module": "dna_interpretation",
    "generated_at": "2026-09-23T10:30:00Z",
    "language": "en",
    "source": {
      "kind": "raw", "format": "23andme", "build": "GRCh37",
      "total_records": 638463, "called": 631022, "no_call_rate": 0.0117,
      "inferred_sex": "female", "panel_total": 334, "panel_found": 291,
      "filename": "genome_raw_data.txt", "warnings": []
    },
    "report": {
      "report_type": "dna",
      "title": "Genetic health report",
      "executive_summary": "Array genotyping with good coverage of the clinical panel...",
      "overall_assessment": {"level": "slightly_elevated", "summary": "Mostly typical findings with a few actionable ones."},
      "data_quality": {"source": "23andMe v5 raw file", "markers_analyzed": 291, "coverage_note": "...", "limitations": "..."},
      "sections": [
        {
          "key": "nutrigenomics", "title": "Nutrient metabolism", "risk_level": "slightly_elevated",
          "summary": "Reduced folate cycle activity...",
          "findings": [
            {"gene": "MTHFR", "rsid": "rs1801133", "genotype": "AG", "phenotype": "C677T heterozygous",
             "risk_level": "slightly_elevated", "evidence": "established",
             "explanation": "About 65% of typical enzyme activity.", "recommendation": "Check homocysteine."}
          ]
        }
      ],
      "pharmacogenomics": [
        {"gene": "CYP2C19", "rsids": ["rs4244285"], "predicted_phenotype": "Intermediate metabolizer",
         "affected_drugs": ["clopidogrel", "omeprazole"], "recommendation": "Review before prescribing clopidogrel."}
      ],
      "disease_risks": [], "carrier_status": [], "nutrigenomics": [], "traits": [],
      "lifestyle_recommendations": ["..."], "recommended_tests": [{"test": "Homocysteine", "reason": "MTHFR C677T"}],
      "red_flags": [], "limitations": "Consumer arrays miss rare variants.",
      "disclaimer": "AI-generated clinical decision support for clinician review; not a diagnosis."
    }
  },
  "timestamp": "2026-09-23T10:30:00Z"
}

Referenz der Antwortfelder

Liest den Upload genau wie /analyze ein und meldet, was gefunden wurde, ohne KI-Aufruf. Authentifizierung ist erforderlich; es wird kein Credit verbraucht. Nutzen Sie den Endpunkt, um eine Datei zu prüfen, bevor Sie einen Credit einsetzen.

Beispielantwort

{
  "status": "success",
  "message": "Payload is valid",
  "data": {
    "valid": true,
    "language": "en",
    "source": {"kind": "raw", "format": "ancestrydna", "build": "GRCh37", "panel_total": 334, "panel_found": 287, "warnings": []}
  }
}

Verbindet einen DNA-Bericht und einen interpretierten Bluttest zu einem gemeinsamen Gesundheitsbericht. Jede Verbindung zwischen einem genetischen Befund und einem Laborwert wird eingeordnet; es folgen eine Risikomatrix, vorrangige Maßnahmen und ein Monitoring-Plan. Ein Credit pro erfolgreicher Anfrage.

Anfrageparameter

Python-Beispiel

# 2) DNA + Blut-Gesundheitsbericht (verwendet run() und dna aus dem Beispiel oben)
blood_test = {
    "parameters": [
        {"short_name": "25-OH D", "result": 18, "unit": "ng/mL", "range_normal_min": 30, "range_normal_max": 100, "evaluation": "low"},
        {"short_name": "Homocysteine", "result": 13.2, "unit": "µmol/L", "evaluation": "high"}
    ]
}
combined = run("/api/v1/dna-blood-report/analyze",
               json={"dna_report": dna, "blood_test": blood_test, "language": "en"})
for link in combined["data"]["report"]["correlations"]:
    print(link["topic"], link["concordance"], link["action"])

Beispielantwort

{
  "status": "success",
  "message": "DNA + blood health report completed successfully",
  "data": {
    "analysis_id": "DNB-8C21E40A9D",
    "module": "dna_blood_report",
    "language": "en",
    "report": {
      "report_type": "dna_blood",
      "executive_summary": "The low vitamin D level matches the GC genotype; homocysteine is borderline, in line with MTHFR C677T.",
      "overall_status": {"level": "watch", "summary": "Two gene-lab matches to act on."},
      "correlations": [
        {"topic": "Vitamin D", "genetic_finding": "GC rs2282679 GT", "lab_finding": "25-OH D 18 ng/mL",
         "concordance": "confirms", "interpretation": "Genetic tendency and lab value agree.", "action": "Supplement and recheck in 12 weeks."}
      ],
      "risk_matrix": [{"area": "Folate cycle", "genetic_risk": "slightly_elevated", "lab_status": "borderline", "combined_assessment": "Watch homocysteine."}],
      "priority_actions": [{"priority": "high", "action": "Start vitamin D3", "why": "Deficient level and GC genotype"}],
      "monitoring_plan": [{"marker": "25-OH vitamin D", "interval": "12 weeks", "reason": "Dose check"}],
      "lifestyle_plan": [], "questions_for_clinician": [], "red_flags": [],
      "limitations": "One blood test; values vary.",
      "disclaimer": "AI-generated clinical decision support for clinician review; not a diagnosis."
    }
  }
}

Referenz der Antwortfelder

Erstellt einen personalisierten Supplement-Plan aus dem DNA-Bericht, den Antworten aus dem Fragebogen und optional einem interpretierten Bluttest. Produktkatalog und Berater-Einstellungen Ihrer Klinik werden automatisch angewendet. Ein Credit pro erfolgreicher Anfrage.

Anfrageparameter

cURL-Beispiel

curl -X POST "https://app.aibloodtestinterpret.com/api/v1/dna-supplements/analyze?async=1" \
  -u "IHR_BENUTZERNAME:IHR_PASSWORT" \
  -H "Content-Type: application/json" \
  -d '{
    "dna_report": { ...data.report aus der DNA-Test-Interpretation... },
    "answers": {"diet_type": "vegetarian", "pregnancy": "no", "sun_exposure": "low", "goals": ["energy", "immunity"]},
    "language": "en"
  }'

Python-Beispiel

# 3) Supplement-Plan (verwendet run(), dna und blood_test aus den Beispielen oben)
plan = run("/api/v1/dna-supplements/analyze",
           json={"dna_report": dna, "blood_test": blood_test,
                 "answers": {"diet_type": "vegetarian", "pregnancy": "no"},
                 "language": "en"})
for item in plan["data"]["report"]["recommendations"]:
    print(item["name"], item["dose"], item["timing"], "-", item["source"])

Beispielantwort

{
  "status": "success",
  "message": "Supplement plan completed successfully",
  "data": {
    "analysis_id": "SUP-51B7D2E6F0",
    "module": "dna_supplements",
    "language": "en",
    "catalogue": {"mode": "prefer", "products": 24},
    "report": {
      "report_type": "supplement",
      "summary": "Plan built from GC and MTHFR findings, a low vitamin D level and a vegetarian diet.",
      "recommendations": [
        {"name": "Vitamin D3 + K2", "source": "clinic_library", "product": "Vitamin D3 + K2 (Clinic Brand)",
         "form": "softgel", "dose": "2000 IU", "timing": "with lunch", "duration": "12 weeks", "priority": "high",
         "rationale": {"genetic": "GC rs2282679 GT", "lab": "25-OH D 18 ng/mL", "questionnaire": "little sun"},
         "evidence": "established", "cautions": ["Recheck calcium"], "interactions": ["Thiazide diuretics"],
         "retest": "25-OH D in 12 weeks"}
      ],
      "avoid_or_caution": [{"name": "High-dose vitamin A", "reason": "Pregnancy planning"}],
      "uncovered_needs": [],
      "dietary_sources": [{"nutrient": "Folate", "foods": ["lentils", "spinach"]}],
      "retest_plan": [{"marker": "25-OH vitamin D", "when": "12 weeks", "why": "Dose check"}],
      "clinician_review_required": ["Thiazide co-medication"],
      "disclaimer": "AI-generated clinical decision support for clinician review; not a diagnosis."
    }
  }
}

Referenz der Antwortfelder

Liest oder aktualisiert den Produktkatalog Ihrer Klinik und die Berater-Einstellungen. Es ist derselbe Katalog wie im Klinik-Panel und in der Ernährungs-KI, sodass ein an einer Stelle hinzugefügtes Produkt überall verfügbar ist. Authentifizierung ist erforderlich; es wird kein Credit verbraucht. Senden Sie catalogue, settings oder beides; der Katalog ersetzt jeweils die gesamte Liste.

cURL-Beispiel

curl -X PUT "https://app.aibloodtestinterpret.com/api/v1/dna-supplements/settings" \
  -u "IHR_BENUTZERNAME:IHR_PASSWORT" \
  -H "Content-Type: application/json" \
  -d '{
    "catalogue": [
      {"name": "Vitamin D3 + K2", "brand": "Clinic Brand", "form": "softgel", "dosage": "2000 IU / 75 µg", "category": "vitamin"},
      {"name": "Omega-3 EPA/DHA", "brand": "Clinic Brand", "form": "softgel", "dosage": "1000 mg", "category": "omega"}
    ],
    "settings": {"mode": "prefer", "instructions": "Prefer our own brand.", "max_items": 8}
  }'

Extrahiert den gesamten Textinhalt aus hochgeladenen Dokumenten mit Kantesti ICR-Technologie. Gibt strukturiertes JSON mit Dokumenttyp-Erkennung, Tabellenextraktion, Abschnittsanalyse und Metadaten zurück.

Anfrageparameter

cURL-Beispiel

curl -X POST "https://app.aibloodtestinterpret.com/api/icr/v1/extract" \
  -F "username=IHR_BENUTZERNAME" \
  -F "password=IHR_PASSWORT" \
  -F "language=de" \
  -F "[email protected]"

Python-Beispiel

import requests

def icr_extract(file_path: str, username: str, password: str, language: str = "de"):
    """
    Text aus einem Dokument mit Kantesti ICR API extrahieren.
    79% schneller und genauer als herkömmliche OCR.
    """
    url = "https://app.aibloodtestinterpret.com/api/icr/v1/extract"

    with open(file_path, "rb") as f:
        files = {"file": (file_path, f)}
        data = {"username": username, "password": password, "language": language}
        response = requests.post(url, files=files, data=data, timeout=120)
        response.raise_for_status()
        return response.json()

# Beispielverwendung
result = icr_extract("medizinischer_bericht.pdf", "benutzername", "passwort", "de")
print(f"Dokumenttyp: {result['data']['document_type']}")
print(f"Seiten: {result['data']['page_count']}")

Beispielantwort

{
  "status": "success",
  "data": {
    "document_type": "blood_test_report",
    "page_count": 1,
    "pages": [
      {
        "page_number": 1,
        "content": {
          "raw_text": "Universitätsklinikum Köln - Blutbild\n\nPatient: Max Mustermann\nGeb.: 15.03.1990\nGeschlecht: Männlich\nDatum: 10.01.2026",
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            {"type": "header", "content": "Universitätsklinikum Köln - Blutbild"},
            {"type": "paragraph", "content": "Patient: Max Mustermann, Geb.: 15.03.1990, Männlich"}
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            {
              "headers": ["Test", "Ergebnis", "Einheit", "Referenzbereich"],
              "rows": [
                ["Glukose", "92", "mg/dL", "74 - 100"],
                ["ALT", "22", "U/L", "< 35"],
                ["Kreatinin", "0,9", "mg/dL", "0,7 - 1,2"],
                ["Leukozyten", "7,2", "10*9/L", "3,8 - 10"],
                ["Hämoglobin", "148", "g/L", "130 - 175"],
                ["Thrombozyten", "230", "10*9/L", "150 - 400"]
              ]
            }
          ]
        }
      }
    ],
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