Ishanu Chattopadhyay

Asst Professsor

ishanu@uchicago.edu

research@paraknowledge.ai

Zero-burden Screening for IPF

via

The Paraknowledge API

Dmytro Onishchenko

UChicago

[
    {
        "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"}
        ]
    }
]

{
  "predictions": [
    {
      "error_code": "",
      "patient_id": "P000012",
      "predicted_risk": 0.005794344620009157,
      "probability": 0.8253881317184486
    }
  ],
  "target": "TARGET"
}

Data In

Data Out

The Paraknowledge API

curl -X POST -H "Content-Type: application/json" -d '[{"patient_id": "P28109965201", "sex": "M", "age": 89, "fips": "35644", "DX_record": [{"date": "12-16-2011", "code": "R09.02"}, {"date": "12-30-2011", "code": "H04.129"}, {"date": "12-30-2011", "code": "H02.109"}], "RX_record": [], "PROC_record": [{"date": "09-28-2012", "code": "71100"}]}]' "https://us-central1-pkcsaas-01.cloudfunctions.net/zcor_predict?target=IPF&api_key=7eea9f70d79c408f2b69847d911303c"

Current Targets

IPF
ILD
ADRD
CKD
CKD_SEVERE
MELANOMA
CANCER_PANCREAS
CANCER_UTERUS
SISA

Cohort Selection and Risk Analysis Testbed

https://paraknowledge.ai/zcor-testbed/

Baseline prevalence of IPF in ILD patients

~25%

ZCoR PPV: 60% @ 50% sensitivity

1310 positive patients from 2183 flags

screen failure:

~70% \(\rightarrow\) 40%

Selection comparison against baseline of 2+ ILD risk factors

baseline prevalence: ~2%

projected screen failure:

~98% baseline \(\rightarrow\) 45%

The Paraknowledge API

curl -X POST -H "Content-Type: application/json" -d '[{"patient_id": "P28109965201", "sex": "M", "age": 89, "fips": "35644", "DX_record": [{"date": "12-16-2011", "code": "R09.02"}, {"date": "12-30-2011", "code": "H04.129"}, {"date": "12-30-2011", "code": "H02.109"}], "RX_record": [], "PROC_record": [{"date": "09-28-2012", "code": "71100"}]}]' "https://us-central1-pkcsaas-01.cloudfunctions.net/zcor_predict?target=IPF&api_key=7eea9f70d79c408f2b69847d911303c"

Current Targets

IPF
ILD
ADRD
CKD
CKD_SEVERE
MELANOMA
CANCER_PANCREAS
CANCER_UTERUS
SISA

Security Compliance

De-identification of PHI: Ensuring all data processed excludes personally identifiable information, adhering to HIPAA's de-identification standards. Uploaded data is never stored, and if PHI is detected, processing is terminated, and data purged from system.
Secure Access with API Keys: Implementing API keys as a method to authenticate and authorize secure access to the system, limiting data access to authorized users only.
HTTPS for Secure Communication: Utilizing HTTPS to encrypt data in transit, safeguarding against interceptions and ensuring data integrity and confidentiality.
Audit Trails: Keeping detailed logs of system access, to monitor and review for unauthorized access or potential breaches.