Ishanu Chattopadhyay PRO
ML Data Science Biomedicine Social Science Faculty
ishanu chattopadhyay
Universal screening for complex diseases
Predictive intelligence for security
Digital Twins for complex systems
Zero-knowledge Discovery
in
Biology, Medicine and Social Systems
Current Products
Electronic Healthcare Record
IPF
ASD
ADRD
Problem: Late or missed diagnosis of serious illnesses
Can we use existing EHR to reliably screen for complex diseases such as pulmonary fibrosis, dementia and rare cancers?
Universal screening for complex diseases
Ai
Ishanu Chattopadhyay
Assistant Professor of Medicine
UChicago
Dmytro Onishchenko
UChicago
Rapid Universal Point-of-care Screening for ILD/IPF Using Comorbidity Signatures in Electronic Health Records
University of Chicago Medicine
NHLBI IPF Stakeholder Summit
Nov 2022
Fernando Martinez, Weill Cornell
Gary Hunninghake
Harvard Med School
Andrew Limper Mayo Clinic
shortness of breath
dry cough
doctor can hear velcro crackles
Common Symptoms
>50 years old
more men than women
IPF
Rare disease
~5 in 10,000
Post-Dx
Survival
~4 years
At least one misdiagnosis
~55%
Two or more misdiagnoses
38%
Initially attributed to age- related symptoms:
72%
Cannot always be seen on CXR
Non-specific symptoms
PCP workflow demands
Initial midiagnoses
~ 4yrs
current
post-Dx survival ~4yrs
~ 4yrs
current clinical DX
ZCoR screening
Onishchenko, D., Marlowe, R.J., Ngufor, C.G. et al. Screening for idiopathic pulmonary fibrosis using comorbidity signatures in electronic health records. Nat Med 28, 2107–2116 (2022). https://doi.org/10.1038/s41591-022-02010-y
n=~3M
AUC~90%
Likelihood ratio ~30
Conventional AI/ML attempts to model the physician
AI in IPF Research
Primary Care
Pulmonologist
ZCoR Flag
ICD administrative codes
IPF
ILD
target codes appear
Past medical history
No target codes appear
case
control
2yrs
2yrs
1YR
IPF drugs prescribed
Signature of IPF diagnostic sequence
pirfenidone or nintedanib
target codes appear
Past medical history
No target codes appear
case
control
2yrs
2yrs
1YR
Truven MarketScan (IBM) Commerical Claims & Encounters Database 2003-2018
>100M patients visible
>7B individual claims
>87K unique diagnostic codes
>7% Medicare data present
2,053,277 patients included in study
University of Chicago Medical Center 2012-2021
68,658 patients
Random sample from Optumlabs Data Warehouse courtsey Mayo Clinic
861,280 patients
2,983,215 patients
performance tables
Marketscan Out-of-sample Results
specificity ~99%
NPV >99.9%
IPF
ILD
performance tables
UCM Out-of-sample Results
specificity ~99%
NPV >99.9%
IPF
ILD
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.
Future
ZCoR 2.0
1
2
3
Deploy as an Epic App
primary care
secondary care
ZCoR
Measure
ZCoR
clinical notes
imaging analytics
The Team
Gary Hunninghake, Pulmonary Care, Harvard Medical School
Fernando Martinez, Pulmonary Critical Care, Weill Cornell
Andrew Limper, Thoracic Research Unit, Mayo Clinic
Dmytro Onishchenko, UChicago
Robert Marlowe,
Medical Comm
Che G. Ngufor
Mayo Clinic
Louis J. Faust
Mayo Clinic
ishanu@uchicago.edu
Current Products
Problem: Can we predict crime at the event-level?
Solution: Deep-Learning without Neural Networks: a new framework designed to learn complex stochastic processes
Problem: Can we predict complex spatio-temporal stochastic processes?
Predictive intelligence for security
Ai
Digital Twins for complex systems
Coupled Evolution of Complex Systems
Predicting Future Mutations, predicting pandemics
Predictive intelligence for security
Digital Twins for complex systems
Universal screening for complex diseases
Current Products
Electronic Healthcare Record
IPF
ASD
ADRD
By Ishanu Chattopadhyay
ML Data Science Biomedicine Social Science Faculty