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
University of Chicago Medicine
University of Chicago Medicine
The Laboratory for Zero Knowledge Discovery
AI/ML earning theory and applications
Complex systems
Implication of AI in Future of Societay
collaborators
Alex Leow
Psychiatry UIC
Anna Podolanczuk, Pulmonary Care, Weill Cornell
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
David Llewellyn
University of Exeter
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
Department of Pediatrics
UChicago
Department of Neurology & The Memory Center
UChicago
Department of Psychiatry
UChicago
Pulmonary Critical Care, Weill Cornell
Department of Anesthesia and Critical Care
UChicago
Center for Health Statistics
UChicago
Pulmonary Critical Care, Harvard Medical School
Department of Psychiatry
UIC
Demon Network, Exeter, Alan Turing Institute, UK
Dalhousie University, Canada
Pritzker School of Molecular ENgineering
Social Science
UChicago
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
Electronic Healthcare Record
IPF
ASD
ADRD
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.
Universal screening for complex diseases
Problem: Event-level prediction in social systems,
e.g. predicting crime before it happens
Predictive intelligence for security
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"?
Digital Twins for complex systems
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?
Digital Twins for complex systems
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
Electronic Healthcare Record
IPF
ASD
ADRD
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.
Universal screening for complex diseases
The need for Universal Screening
Takes too long,
not supported by insurance,
"gut feeling" / "wait & see" common
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
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
ICD administrative codes
IPF
ILD
target codes appear
Past medical history
No target codes appear
case
control
2yrs
2yrs
1YR
Use ICD codes to determine cases and controls
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
ICD Codes can be noisy
"cases" are not always true IPF
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:
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.
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
ZED performance | Competition | |
---|---|---|
Autism | >80% AUC at 2 yrs | Double false positives |
Alzheimer's Disease | ~90% AUC | 60-70% AUC |
Idiopathic Pulmonary Fibrosis | ~90% AUC | NA |
MACE | ~80% AUC | ~70% AUC |
Bipolar Disorder | ~85% AUC | NA |
CKD | ~85% AUC | NA |
Cancers | ~75% AUC | NA |
1 in 59
Autism Spectrum Disorder
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
MCHAT/F
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
MOCA, Blood Tests
Current Practice:
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
Predictive intelligence for security
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
The Social Concerns of AI in Policing
The Underlying Math
Hotspots?
Very different from past efforts
Not based on standard "Deep Learning"
The Underlying Math
Not based on standard "Deep Learning"
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
1 Week in advance
Within ~2 city blocks
ONLY Past eventlog as input
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
10 crimes
Raise 11 flags
8 correct flags
3 false positives
2 false negatives
Mean AUC
Property crime: 81%
Violent crime: 84%
sensitivity 0.88
ppv 0.91
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
Play Movie
Triangles: actual events
heatmap: predicted risk 3 days ahead
Could we have predicted this?
Double homicide
Jan 7 2019
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
Not just a predictor
Digital Twin of social interactions
Predict policy effects
Precise predictation
Discover and quantify biases
Problem: Can we predict the next pandemic?
Can we predict future mutations? Can we define the "edge of emergence"?
Digital Twins for complex systems
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
Problem: Can AI predict how we think and interact?
Can we predict how opinions evolve?
Digital Twins for complex systems
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).
By Ishanu Chattopadhyay
AI in Bio-med-social problems
ML Data Science Biomedicine Social Science Faculty