ishanu chattopadhyay

Universal screening for complex diseases

Predictive intelligence for security

Digital Twins for complex systems

Current Products

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

University of Chicago Medicine

NHLBI IPF Stakeholder Summit

Nov 2022

shortness of breath

dry cough

doctor can hear velcro crackles

Common Symptoms

>50 years old

more men than women

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

• Co-morbidity Patterns
• No data demands
• Use whatever data is already in patient file

• Discover and leverage comorbidity patterns
• No data demands
• Use whatever data is already on patient file

Primary Care

Pulmonologist

ZCoR Flag

ICD administrative codes

IPF

ILD

IPF drugs prescribed

Signature of IPF diagnostic sequence

pirfenidone or nintedanib

• age > 50 years
• at least two IPF target codes identified at least 1 month apart 
• chest CT procedure (ICD-9-CM 87.41 and Current Procedural Terminology, 4th Edition, codes 71250, 71260 and 71270) before the first diagnostic claim for IPF
• no claims for alternative ILD codes occurring on or after the first IPF claim

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: 

• Heathcare Capacity

Ethics:

• Risk from Imaging Tests

• General likelihood ratio 60-80
• PPV 3.5-5%

• Notifying patients 4 years early?

• No cure, why screen

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.

• Early anti-fibrotic therapy seems increasingly promising

• Better shot at lung transplant

• Early dx reduces  hospital-izations by a factor of 1-3

Future

ZCoR 2.0

1

2

3

Deploy as an Epic App

primary care

secondary care

ZCoR

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

Coupled Evolution of Complex Systems

Predicting Future Mutations, predicting pandemics

Current Products