ZeBRA Model for SISA Screening
You can run ZeBRA using API call
Specify target (M2550, F2550, M5075, or F5075) to run ZeBRA for different patient cohorts.
!curl -X POST -H "Content-Type: application/json" -d @SAMPLE_INPUT/M2550.json "https://us-central1-pkcsaas-01.cloudfunctions.net/zebra-predict-sisa?target=M2550&api_key=63d87942b4a2d61985a377db05a35e73"
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- "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).
[
{
'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'},
]
}
][
{
'patient_id': 33443873802,
'predicted_risk': 0.00353191883909529,
'error_code': '',
'probability': 0.9696941114273572
}
]- Each record must span at least 1 year 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.