zebra_adrd

ZeBRA Model for ADRD Screening

You can run ZeBRA ADRD using API call

zebra-predict-adrd2026

Single Patient Example

curl -X POST -H "Content-Type: application/json" -d '[{"patient_id": "000012", "sex": "M", "age": 89, "birth_date": "01-01-1921", "fips": "35644", "DX_record": [{"date": "01-05-2012", "code": "G35"}, {"date": "02-02-2012", "code": "H35.359"}, {"date": "03-29-2012", "code": "G35"}, {"date": "04-05-2012", "code": "R94.09"}, {"date": "04-05-2012", "code": "G35"}, {"date": "06-21-2012", "code": "G35"}], "RX_record": [], "PROC_record": [{"date": "03-29-2012", "code": "72170"}]}]' "https://us-central1-pkcsaas-01.cloudfunctions.net/zebra-predict-adrd2026?api_key=63d87942b4a2d61985a377db05a35e73"

Multiple patient data in file

curl -s -X POST -H "Content-Type: application/json" -d @sample_input.json "https://us-central1-pkcsaas-01.cloudfunctions.net/zebra-predict-adrd2026?api_key=63d87942b4a2d61985a377db05a35e73"

Input Format

Input is provided as a JSON file containing a list of patient records. Each patient record is represented as a JSON object with the fields below.

• patient_id - numeric value, can be deidentified ID.
• birth_date - Format: "MM-DD-YYYY". Does not have to be exact date of birth, but has to be approximate enough to estimate patient's age.
• sex (required) - "M" and "F" values are accepted.
• DX_record - A list of dictionaries, each containing a date of diagnosis in "MM-DD-YYYY" format (date), and a diagnostic code in ICD-10 or ICD-9 format (code).
• RX_record - A list of dictionaries, each containing a date of prescription in "MM-DD-YYYY" format (date), and a prescription code in NDC (National Drug Code) format (code).
• PROC_record - A list of dictionaries, each containing a date of procedure in "MM-DD-YYYY" format (date), and a procedural code in CPT, HCPCS, or ICD format (code).

Example Input (valid JSON)

[
    { 
        'patient_id': 33443873802,
        'birth_date': '01-01-2006',
        'sex': 'F',
        'DX_record': [
            {'date': '07-31-2006', 'code': 'Z38.00'},
            {'date': '08-07-2006', 'code': 'Z00.129'},
            {'date': '08-07-2006', 'code': 'P59.9'},
            {'date': '08-29-2016', 'code': 'J01.90'}
        ],
        'RX_record': [
            {'date': '10-29-2011', 'code': '61168010101'},
            {'date': '05-16-2015', 'code': '00071439703'},
            {'date': '08-08-2015', 'code': '00005501523'},
        ],
        'PROC_record': [
            {'date': '02-05-2007', 'code': '90723'},
            {'date': '11-05-2007', 'code': 'J1100'},
            {'date': '11-05-2007', 'code': '99214'},
        ]
    }
]

Example output for one patient:

[
    {
        'patient_id': 33443873802,
        'predicted_risk': 0.00353191883909529,
        'error_code': '',
        'probability': 0.9696941114273572
    }
]

Input Data Requirements

• Each record must span at least 52 weeks between the earliest and latest diagnosis dates in DX_record. Records with shorter diagnostic histories are not assessed in order to ensure sufficient longitudinal context for feature construction.