Ishanu Chattopadhyay PRO
ML Data Science Biomedicine Social Science Faculty
Third-wave AI in Medicine:
From Test-free Screening of Complex Diseases
to
Digital Twins of Microbiomes and Pandemics
Ishanu Chattopadhyay, PhD
Assistant Professor of Medicine
University of Chicago
ishanu@uchicago.edu
MEDIA
Cohort Selection and Risk Analysis Testbed
Misleading Diagnosis of Idiopathic Pulmonary Fibrosis: A Clinical Concern
Javier Ramos-Rossy, MD, Onix Cantres-Fonseca, MD, Ginger Arzon-Nieves, Yomayra Otero-Dominguez, MD, Stella Baez-Corujo, MD, and William Rodríguez-Cintrón, MD
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
ADRD
CKD
MELANOMA
CANCER_PANCREAS
CANCER_UTERUS
SISA
performance tables
Out-of-sample Results
specificity ~99%
NPV >99.9%
IPF
ILD
Comorbidity Spectra
False Positives:
Ethics:
For every 20-30 flags,
1 is positive
minimal
acceptable?
Better outcomes
Collard, Harold R., Alex J. Ward, Stephan Lanes, D. Cortney Hayflinger, Daniel M. Rosenberg, and Elke Hunsche. "Burden of illness in idiopathic pulmonary fibrosis." Journal of medical economics 15, no. 5 (2012): 829-835.
Application to Suicide Attempts and Ideation (SISA) , PTSD*:
perhaps surprising connection between mood disorders and physiological comorbidities
Gibbons RD, Kupfer D, Frank E, Moore T, Beiser DG, Boudreaux ED. Development of a Computerized Adaptive Test Suicide Scale-The CAT-SS. J Clin Psychiatry. 2017 Nov/Dec;78(9):1376-1382. doi: 10.4088/JCP.16m10922. PMID: 28493655.
* in press
Predictions at the Point-of-Diagnosis
Can my patient continue taking anti-fibrotics over long term?
Digital Twins for Health trajectories
}
1M parameters
1M parameters
Predicts disorders across the disease specturm
Pre-empting Effectiveness of Antifibrotics at the point of diagnosis
~78% AUC
26-32 out of 100 discontinued
4-5 out of 100 discontinued
Prognosis at Point-of-Diagnosis
Patient Journey
Early Diagnosis
Reduce screen failure rates
Holistic health surveillance
Predict antifibrotics continuation
improve outcomes
3
2
1
Feeding Variables added
A Math Solution to a Hard Biological Problem
we can tell if new strain will adapt to humans
By Ishanu Chattopadhyay
Predictive modeling of crime and rare phenomena using fractal nets
ML Data Science Biomedicine Social Science Faculty