ishanu chattopadhyay

Universal screening for complex diseases

Predictive intelligence for security

Digital Twins for complex systems

University of Chicago Medicine

University of Chicago Medicine

The Laboratory for Zero Knowledge Discovery

collaborators

Alex Leow

Psychiatry UIC

Gary Hunninghake, Pulmonary C, Harvard

Robert Gibbons, Hospital Medicine

Daniel Rubins, Anesthesia and Critical Care

Peter Smith, Pediatrics

Michael Msall Pediatrics

Fernando Martinez, Pulmonary Critical Care, Weill Cornell

James Mastrianni, Neurology

James Evans, sociology

Erika Claud, Pediatrics

Aaron Esser-Kahn Molecular Engineering

Kenneth Rockwood

Dalhousie University

Andrew Limper Mayo Clinic

zed.uchicago.edu

• Dr. Shahab Asoodeh

• Dr. Yi Huang

• Dmytro Onishenko

• Victor Rotaru

• Jin Li

• Ruolin Zhang

• David Yang

• Dr. Nicholas Sizemore

• Drew Vlasnik

• Lucas Mantovani

• Jaydeep Dhanoa

• Jasmine Mithani

• Angela Zhang

• Warren Mo

people

zed.uchicago.edu

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?

Ai

Onishchenko, Dmytro, Robert J. Marlowe, Che G. Ngufor, Louis J. Faust, Andrew H. Limper, Gary M. Hunninghake, Fernando J. Martinez, and Ishanu Chattopadhyay. "Screening for idiopathic pulmonary fibrosis using comorbidity signatures in electronic health records." Nature Medicine 28, no. 10 (2022): 2107-2116.

Problem: Event-level prediction in social systems,

e.g. predicting crime before it happens

Ai

Can we predict complex spatio-temporal stochastic processes?

Rotaru, Victor, Yi Huang, Timmy Li, James Evans, and Ishanu Chattopadhyay. "Event-level prediction of urban crime reveals a signature of enforcement bias in US cities." Nature human behaviour 6, no. 8 (2022): 1056-1068.

Problem: Can we predict the next pandemic?

Can we predict future mutations? Can we define the "edge of emergence"?

Chattopadhyay, Ishanu, Kevin Wu, Jin Li, and Aaron Esser-Kahn. "Emergenet: Fast Scalable Pandemic Risk Assessment of Influenza A Strains Circulating In Non-human Hosts." (2023). Under Review in Nature

PREEMPT

Problem: Can AI predict how we think and interact?

Can we predict how opinions evolve?

YFA 2020

Can an AI tell if you are lying?

Can an AI tell how you are going to vote?

Yang, David, James EVans, and Ishanu Chattopadhyay. "‘Its the Economy Stupid’: Predictive Theory of Belief Shift Connecting Economic Stress to Societal Polarization." (2023).

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?

Ai

Onishchenko, Dmytro, Robert J. Marlowe, Che G. Ngufor, Louis J. Faust, Andrew H. Limper, Gary M. Hunninghake, Fernando J. Martinez, and Ishanu Chattopadhyay. "Screening for idiopathic pulmonary fibrosis using comorbidity signatures in electronic health records." Nature Medicine 28, no. 10 (2022): 2107-2116.

The need for Universal Screening

• Often the problem is not that diseases cannot be diagnosed by physicians, but one of missed or late diagnoses in the primary care workflow

Takes too long,

not supported by insurance,

"gut feeling" / "wait & see" common

• Universal screening for many diseases are non-existant

Is AI/ML  adding anything of  relevance?

"predicting" autism > 3yrs

"diagnosing" fibrosis from lung imaging

"diagnosing" dementia from  brain scan

Rapid Universal Point-of-care Screening for ILD/IPF Using Comorbidity Signatures in Electronic Health Records

Flag patients before they (or doctors) suspect 

Primary Care

Pulmonologist

?

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

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

very likelihood ratios achieved irrespective of subgroup

performance tables

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

Clinical Trial Cohort Selection

Current screen failure rate ~50-60%

ZCoR boosted screen failure rate ~20%

Longitudinal history is important, cannot simply process snapshots

Comparison of ZCoR with off-the-shelf AI

Leveraging Longitudinal  Patterns

Specialized HMM models from code sequences

Model control and case cohorts seprately

given a new test case, compute likelihood of sample arising from case models vs control models

sequence likelihood defect

Future

ZCoR 2.0

1

2

3

Deploy as an Epic App

primary care

secondary care

ZCoR

ZCoR

clinical notes

imaging analytics

Optimize implementation

ZeD Lab: Predictive Screening from Comorbidity Footprints

Nature Medicine

JAHA

CELL Reports

Science Adv.

ZeD Lab: Predictive Screening from Comorbidity Footprints

1 in 59

Children with ASD experience higher co-morbidities

Can we exploit these patterns to predict diagnosis?

Common Knowledge: Comorbidties  Exist

source: IBM Marketscan data

Autism Co-morbid Risk (ACoR) Score

Data: Onishchenko etal. Science Advances 2021

Autism Co-morbid Risk (ACoR) Score

Joint Operation with MCHAT/F

reduce false positives by 50%

OR

boost sensitivity by 100%

Data: Onishchenko etal. Science Advances 2021

standalone operation

Alzheimer's Disease and Related Dementia*

* in press

>5 Million in US. >13 Million in next 10 years

Alzheimer's Disease and Related Dimentia

state of art with EHR:

~67% AUC*

ZCoR:  ~87%

Alzheimer's Disease and Related Dimentia

state of art with EHR:

~67% AUC*

ZCoR:  ~87%

Preempting ADRD accurately upto a decade in future

Applicable To Screening for Mild Cognitive Impairment

Clinical Trial Participant Selection

Current screen-failure rate: 80-90%

Estimated rate with ZCoR:

40%

Application to Suicide Attempts and Ideation (SISA): 

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

Application to Malignant Neoplasms*

Melanoma

Melanoma has a high survival rate of over 90% when treated early. But if it progresses to later stages, the survival rate drops significantly. Identifying potentially life-threatening melanomas is crucial.

* in press

Problem: Event-level prediction in social systems,

e.g. predicting crime before it happens

Ai

Can we predict complex spatio-temporal stochastic processes?

Rotaru, Victor, Yi Huang, Timmy Li, James Evans, and Ishanu Chattopadhyay. "Event-level prediction of urban crime reveals a signature of enforcement bias in US cities." Nature human behaviour 6, no. 8 (2022): 1056-1068.

The Problem of Free Will

in

social

behavior

prediction

• Predictions cannot be self-fulfilling prophecies
• Transparency
• Explore the interaction between crime, social factors and enforcement

• Ability to predict law enforcement response to crime as well
• Analyze if such reponses reveal existant policy biases

The Underlying Math

Very different from past efforts

Not based on standard "Deep Learning"

The Underlying Math

Not based on standard "Deep Learning"

• Forecasting rare events in  multi-variable stochastic evolution requires new modeling architecture​​

• Learn local "activation functions" as symbolic probabilistic transducers​

• Assemble these local predictors into a "fractal net"

Applies to any rare/extreme event phenomena

Ishanu Chattopadhyay, Yi Huang, James Evans et al. Deep Learning Without Neural Networks: Fractal-nets for Rare Event Modeling, 26 October 2020, PREPRINT (Version https://doi.org/10.21203/rs.3.rs-86045/v1

Predicting crime sufficiently ahead of time to be actionable

• Prediction precise enough in time and space to be actionable
• Use ONLY data that is realistically and cheaply available

93% accuracy

87% AUC

~70% specificity at ~80% sensitivity

Chicago Predictive Performance

10 actual crimes:

11 predicted:

8 correct:

2 missed:

3 false alarms

Boston Districts: B2 B3 C1

Jan 1 2021 -  July 22 2022

Total # of events: 7419

Boston

Mean AUC

Property crime: 81%

Violent crime:    84%

Spatial tiles:

0.003 deg latitude, 0.003 deg longitude

0.25 miles across

Time-period:

Training:                      Jan 1 2016 - Dec 31 2018

Out-of-sample test:  Jan 1 2019 - April 1 2019

Philadelphia

sensitivity   0.90
ppv           0.87

100 crimes

Raise 103 flags

90 correct flags

13 false positives

10 missed

3 day ahead prediction

Jan 1 2019

to

April 1 2019

Triangles: actual events

heatmap: predicted risk 3 days ahead

Triple homicide incident

Jan 7 2019

https://www.inquirer.com/crime/kensington-triple-shooting-homicide-philadelphia-police-20190107.html

Triangles: actual events

heatmap: predicted risk 3 days ahead

Digital Twin

Problem: Can we predict the next pandemic?

Can we predict future mutations? Can we define the "edge of emergence"?

Chattopadhyay, Ishanu, Kevin Wu, Jin Li, and Aaron Esser-Kahn. "Emergenet: Fast Scalable Pandemic Risk Assessment of Influenza A Strains Circulating In Non-human Hosts." (2023). Under Review in Nature

PREEMPT

BioNorad