For creating a new username and retrieving an API key, visit ParaKnowledge AI. For support, contact research@paraknowledge.ai.
curl -X POST -H "Content-Type: application/json" -d '[{"patient_id": "P000012", "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/zcor_predict?target=IPF&api_key=APIKEY"
curl -X POST -H "Content-Type: application/json" -d '[{"patient_id": "P000012", "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/zcor_predict?target=ILD&api_key=APIKEY"
curl -s -X POST -H "Content-Type: application/json" -d @sampledata/patients_10.json "https://us-central1-pkcsaas-01.cloudfunctions.net/zcor_predict?target=IPF&api_key=APIKEY"
Replace APIKEY with a valid api-key obtained by emailing support.
zcor_predict is an EHR-based predictive algorithm designed to screen for a range of disorders using electronic health records. This tool currently supports screening for the following targets:
- IPF (Idiopathic Pulmonary Fibrosis)
- ILD (Interstitial Lung Disease)
- ASD (Autism Spectrum Disorder, only for young children)
- ADRD (Alzheimer's Disease and Related Dementia) pending
- IPF - 88% AUC for predicting risk of IPF diagnosis 1 year ahead. Onishchenko et al., Nature Medicine
- ASD - 80% AUC for predicting risk of future ASD diagnosis in 2-year-olds. Onishchenko et al., Science Advances
- ADRD - 88% AUC for predicting risk of ADRD diagnosis 1 year ahead.
zcor_predict is deployed as a cloud function and can be accessed via the following URL endpoint:
https://us-central1-pkcsaas-01.cloudfunctions.net/zcor_predict?target=TARGET&api_key=YOUR_API_KEY
Replace TARGET with either IPF or ASD, and YOUR_API_KEY with your actual API key. The service responds to POST requests containing patient data encoded as a JSON object.
zcor_predict_fast
https://us-central1-pkcsaas-01.cloudfunctions.net/zcor_predict_fast?target=TARGET&api_key=YOUR_API_KEY
The input should be a JSON object that consists of a list of dictionaries. Each dictionary contains a single patient record with the following fields:
patient_id(required)birth_date(required) - Format: "MM-DD-YYYY".sex(optional) - Defaults to "F" if not provided.DX_record(required) - A list of dictionaries containing the patient's diagnostic codes in ICD10 or ICD9 format (code), along with the date of diagnosis (date).RX_record(optional, required for certain targets) - A list of dictionaries with the patient's prescription codes in NDC format (code), and the date of prescription (date).PROC_record(optional, required for certain targets) - A list of dictionaries with the patient's procedural codes in ICD, CPT, or HCPCS format (code), and the date of the procedure (date).
- Essential fields:
patient_id,birth_date, andDX_record. DX_recordmust have at least 2 diagnosis codes, recorded at least 1 week apart.- We recommend a minimum 5 diagnosis codes in medical history for flags to be reliable
The service returns a JSON object with predictions, structured as follows:
{
"predictions": [
{
"error_code": "",
"patient_id": "P000012",
"predicted_risk": 0.005794344620009157,
"probability": 0.8253881317184486
}
],
"target": "TARGET"
}In this structure, TARGET will be either IPF or ASD, depending on the specified target in the request. Each entry in the predictions array corresponds to a patient's risk prediction for the specified disorder.
The probability is the probability of a patient to experience the target disorder in near future (the length of this future depends on the model, but currently the "prediction window" is 1 year).
We recommend that for relable prediction, use decision thersholds greater than 90% or higher on this estimated probability.
[
{
"patient_id": "P000038",
"sex": "F",
"birth_date": "01-01-2006",
"DX_record": [
{"date": "07-31-2006", "code": "Z38.00"},
{"date": "08-07-2006", "code": "P59.9"},
{"date": "08-29-2016", "code": "J01.90"},
{"date": "09-10-2016", "code": "J01.90"},
{"date": "11-14-2016", "code": "J01.91"}
],
"RX_record": [
{"date": "10-29-2011", "code": "rxLDA017"},
{"date": "05-16-2015", "code": "rxIDG004"},
{"date": "08-08-2015", "code": "rxIDG004"},
{"date": "06-04-2016", "code": "rxIDD013"}
],
"PROC_record": [
{"date": "02-05-2007", "code": "90723"},
{"date": "11-05-2007", "code": "J1100"}
]
}
]| Disease | Runtime (s) for 1 Patient | Runtime (s) for 1000 Patients |
|---|---|---|
| ADRD | 27.8825 | 1359.7784 |
| ASD | 11.6790 | 371.0477 |
| ILD | 26.5180 | 610.5329 |
| IPF | 12.9410 | 465.9096 |
For larger number of patients, the time perpatient is approximatley 1 second or less for most targets.
For information on pricing and plans, please contact our support team at research@paraknowledge.ai. We offer various pricing models to cater to different needs, including per-use, subscription-based, and enterprise solutions.
- Regular updates and maintenance schedules are posted on our website.
- For API integration and technical queries, consult our detailed API Documentation.
- Users are encouraged to report any bugs or issues via our support email.