UTAH-ChkTlk

Third Wave AI in Medicine:

From Test-free Screening of Complex Diseases

to

Understanding Microbiome Self-organization and Zoonotic Emergence

Ishanu Chattopadhyay, PhD

Assistant Professor of Medicine

University of Chicago

ishanu@uchicago.edu

zed.uchicago.edu

D3M (I2O)

PAI (DSO)

PREEMPT (BTO)

YFA (DSO)

NIA

Zero-burden EHR Analytics

Diagnostic & Screening for complex disorders

*CoR : * Comorbid Risk Scores

ACoR (Autism)

PCoR (IPF/ILD)

ZCoR (ADRD/AD)

ZCoR-C (cancers with further specialization)

Clinical Trial Cohort Selection

Current screen failure rate ~50-60%

ZCoR boosted screen failure rate ~20%

AMGEN

Cloud Deployment

[
    {
        "patient_id": "P000038",
        "sex": "F",
        "birth_date": "01-01-2006",
        "DX_record": [
            {"date": "07-31-2006", "code": "Z38.00"},
            {"date": "08-07-2006", "code": "P59.9"},
            {"date": "08-29-2016", "code": "J01.90"},
            {"date": "09-10-2016", "code": "J01.90"},
            {"date": "11-14-2016", "code": "J01.91"}
        ],
        "RX_record": [
            {"date": "10-29-2011", "code": "rxLDA017"},
            {"date": "05-16-2015", "code": "rxIDG004"},
            {"date": "08-08-2015", "code": "rxIDG004"},
            {"date": "06-04-2016", "code": "rxIDD013"}
        ],
        "PROC_record": [
            {"date": "02-05-2007", "code": "90723"},
            {"date": "11-05-2007", "code": "J1100"}
        ]
    }
]

{
  "predictions": [
    {
      "error_code": "",
      "patient_id": "P000012",
      "predicted_risk": 0.005794344620009157,
      "probability": 0.8253881317184486
    }
  ],
  "target": "TARGET"
}

Data In

Data Out

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
ILD
ADRD
CKD
CKD_SEVERE
MELANOMA
CANCER_PANCREAS
CANCER_UTERUS
SISA

Cohort Selection and Risk Analysis Testbed

https://paraknowledge.ai/zcor-testbed/

https://paraknowledge.ai/zcor-demo/

Cohort Selection and Risk Analysis Testbed

https://paraknowledge.ai/zcor-testbed/

https://paraknowledge.ai/zcor-demo/

melanoma+dementia

Baseline prevalence of IPF in ILD patients

~25%

ZCoR PPV: 60% @ 50% sensitivity

1310 positive patients from 2183 flags

screen failure:

~70% \(\rightarrow\) 40%

curl -X POST https://us-central1-pkcsaas-01.cloudfunctions.net/emergenet_predict?api_key=7eea9f70d79c408f2b69847d6911303c -H "Content-Type: application/json" -d @alberta.json

Point-of-care for ASD

Autism Co-morbid Risk (ACoR) Score

Data: Onishchenko etal. Science Advances 2021

Autism Co-morbid Risk (ACoR) Score

MCHAT/F

Head to head comparison with current practice

Data: Onishchenko etal. Science Advances 2021

PA-21-200 - Research on Autism Spectrum Disorders (R21)

NIMH

Aim 1: Assess prospective performance of ACoR used as a standalone tool, as well as when combined with existing tools such as M-CHAT/F, when applied to children within the age of 18-26 months, typically around their two-year wellness check visits. Since the existing screening tools are questionnaire based while ACoR leverages subtle predictive patterns emerging in medical history, these tools are expected to be functionally independent, and may be combined resulting in substantial boost in performance. We will confirm these performance gains via a gold-standard confirmatory diagnosis (ADOS-2) following the ACoR and M-CHAT/F scoring. Patients recruited with informed consent will be all scored with M-CHAT/F, ACoR and ADSO-2 to estimate false positive and false negative rates.

Aim 2: Examine ACoR’s efficacy in a diverse population with socio-economic, demographic, and other co-founders, via a comprehensive interdisciplinary assessment. Additionally, we will collect data on familial socio-economic status (SES)34 and indicators for adverse childhood experiences (ACEs)35,36, measured by parent response to questions about children’s exposure to neighborhood violence, racial/ ethnic discrimination, income hardship, parental divorce, incarceration, death, domestic violence, substance abuse and mental illness. These data will be analyzed to estimate the impact of race, ethnicity, demographics, and other typical confounders on ACoR estimated risk, elucidating the impact of the proposed tool on health equity considerations, and the current lack of diagnostic parity across marginalized communities.

Aim 3: Determine how developmental and learning disorders such as intellectual delay impact ACoR performance. Within this aim we will determine comorbidities of motor, communicative, regulatory behavior, and adaptive skills among ACoR identified children with ASD compared to M-CHAT/F-positive children with and without ASD. We hypothesize that the frequency of delays in gross and fine motor skills, receptive and express language skills, verbal, and nonverbal cognitive skills, internalizing and externalizing regulatory and social behavior skills, and criterion specific adaptive skills (feeding, dressing, toilet learning, self-mobility, social interactions) will show interpretable trends associated with elevated ACoR score.

March 11, R21, Co-PI: Michael Msall, Uchicago

2 yrs, 450K

Point-of-care for IPF/ILD

Data: Onishchenko etal. Nat. Medicine 2022

patient A

patient B

patient C

lesson 1

Beyond "risk factors" to personalized risk patterns

False Positives:

Heathcare Capacity

Ethics:

Risk from Imaging Tests

For every 20-30 flags,

1 is positive

General likelihood ratio 60-80
PPV 3.5-5%

Notifying patients 4 years early?

No cure, why screen

minimal

acceptable?

Better outcomes

early anti-fibrotic therapy seems increasingly promising

better shot at lung transplant

early dx reduces hospital-izations by a factor of 1-3

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.

Aim 1: Validate ZCoR for flagging ILD, IPF and PrePF Prospectively In a Pragmatic Clinical Trial. Validate
ZCoR as a reliable primary-care screening tool for ILD/IPF.

Aim 2: Performance in High Risk Cohorts Within this aim we plan to evaluate the applicability and
effectiveness of ZCoR in high risk cohorts, treated in participating pulmonary care clinics over the last 5 years.
This includes patients diagnosed with or in the diagnostic workup for suspected COPD or Lung Cancer, or
other relevant pulmonary disorders.

Aim 3: Developing and Validating ZCoR Visual Risk-explainer (VisPF module) Enabling Drill-down well?
and AI-driven Predictors of Anti-fibrotic Treatment Responders Within this aim we plan to develop and
validate the capability for inspecting explanations for the AI estimated risk for individual patients, answering
“why” the AI estimated a patient to have high/low risk. This explanatory capability can shed new light on
IPF/ILD heterogeneity, and potentially identify new functional phenotypes, especially aiming to predict patient
phenotype that indicates favorable response to anti-fibrotics and other disease modifying therapy.

June 5, R01, Co-PI: Fernando Martinez, Cornell

NOT-CA-24-031 Validation of Digital Health and Artificial Intelligence/Machine Learning Tools for Improved Assessment in Biomedical and Behavioral Research

NHLBI

5 yrs R01

3.5M

Point-of-care for ADRD/AD

>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

Aim 1: Validate ZCoR for flagging ADRD/AD

Aim 2: EHR integrated deployment, and carry out usability and physician confidence

Aim 3: Develop ZCoR 2.9, combining EHR data with CSF markers, genomics and demographics

June 5, R01, Co-PI: James Mastrianni (?)

NIA (Investigator Initiated/ NOT-CA-24-031 Validation of Digital Health and Artificial Intelligence/Machine Learning Tools for Improved Assessment in Biomedical and Behavioral Research

R01

3.5M

Point-of-care for SISA/PTSD

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

Towards Continuous Monitoring of Psychological Health.

Specific Aim 1: Development of AI-Mediated Pipeline for PTSD and SI/SA.

Specific Aim 2: Investigate how past TBI modulates the future risk of experiencing SI/SA and PTSD

Specific Aim 3: Limited Prospective Validation in a Clinical Study

Co-PI: Robert Gibbons + (?)

CDMRP 2024: Traumatic Brain Injury and Psychological Health Research Program

DoD

4 yrs

2M

LUKInGLAss:

Layered Universal Knowledge INtegration for Guided Longitudinal Assessment

AI-enabled Reconfigurable Primary-care Continuous Screening for Complex Disorders

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.

LUKInGLAss (Layered Universal Knowledge INtegration for Guided Longitudnal Assessment) framework for AI-enabled Reconfigurable Primary-care Continuous Screening for Complex Disorders

PCORI

Microbiome Modeling

Microbiome profiles to screen for complex disorders; especially when long term medical history is not available

Microbial transplants tuned to individuals: Precision medicine with microbiome

Just add missing microbes back?

Not all perturbations are valid

We have theoretical predictions, which can be tested in bioreactors

Supplantation MUST be personalized

Supplantation MUST be personalized

Supplantation MUST be personalized

Emergence Modeling

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

How do we measure "distance" between strains?

\Phi_i:\prod_{j \neq i} \Sigma_j \rightarrow \mathcal{D}(\Sigma_i)

Q-Net

recursive forest

Influenza Risk Assessment Tool (IRAT) scoring for animal strains

Can we replicate IRAT scores*?

slow (months), quasi-subjective, expensive

*https://www.cdc.gov/flu/pandemic-resources/monitoring/irat-virus-summaries.htm

genomic analysis

receptor binding

animal

transmission

antivirals available

population immunity

human infections

animal

hosts

global prevalence

antigenic novelty

disease severity

Influenza Risk Assessment Tool (IRAT) scoring for animal strains

slow (months), quasi-subjective, expensive

*https://www.cdc.gov/flu/pandemic-resources/monitoring/irat-virus-summaries.htm

24 scores in 14 years

~10,000 strains collected annually

Emergenet: finding emergence risk of animal strains

Emergenet time: 1 second

BioNorad

BioNORAD: Fast Scalable Pandemic Risk Assessment of Influenza A Strains Circulating In Non-human Hosts

Aim 1: Develop a platform powered by novel pattern discovery and recognition algorithms to automatically parse out emergent evolutionary constraints operating on Influenza A viruses in the wild, to provide a less-heuristic theory-backed scalable solution to emergence prediction. We plan to show that this capability enables preempting strains which are expected to be in future human circulation, and approximate IRAT scores of non-human strains without experimental assays or SME scoring, in second as opposed to weeks or months. Our approach automatically takes into account the time-sensitive variations in selection pressures as the background strain circulation changes over time, and will potentially be able to rank-order strains adapatively.

Aim 2: Validate our ability to predict future variations of viral proteins by showing that predicted variants of HA and NA fold correctly, and are functional, binding to the relevant human receptors in in-vivo laboratory experiments. Thus, bringing together rigorous data-driven modeling, and validation via tools from reverse genetics we plan to deliver an actionable and deployable platform that optimally exploits the current biosurvellance capacity.

FY24 PRMRP Portfolio Category: Infectious Diseases | FY24 PRMRP Topic: proteomics
| FY24 PRMRP Strategic Goal: Epidemiology: Identify strategies for surveillance or develop modeling tools and/or biomarkers to predict outbreaks or epidemics

Factitious and Simulated Symptom Detection in Mental Health DIagnosis

VeRITaAS

Vetting Response Integrity from
cross-Talk in Adversarial
Surveys

VeRITAS

High Complexity

Low Surprise

Responses that are reflective of symptoms

structured interview

properties of true responses

1

2

3

Generalizable Rapidly Reconfigurable Lie-detector

Minimum AUC = \(0.95 \pm 0.005\)

Cannot be coached, or memorized

Number of possible responses

10^{25}

Minimum Performance (n=624)

Average Time: 3.5 min

No. of Items: 20

AUC > 0.95

PPV > 0.86

NPV > 0.92

At least 83.3% sensitivity at 94% specificity

Beat the test!

paraknowledge.ai/veritas

DARPA

DSO

Habitus Program (in discussion)

Aim: Identify veracity of interviews without asking aggressive questions

Substance Abuse Disorder

\kappa

\nu

malingering

SUD

No SUD

Cook County Data

Estimated malingering rate 0.34

NIMH R01 (Co-PI, with Michael Brooks NW)

Substance Abuse Disorder

TITLE: Algorithmic Detection of Adversarial Responses to Computer-aided Diagnostic Tests for Mental Disorders

Aim: Use inferred cross-dependencies between responses to items selected adaptively from a question-bank
designed for diagnosis of mental disorders (CAD) to identify malingering/non-attentive participants, developing an
algorithmic lie detector that is impossible to cheat with psychiatric training.

Social Modeling

Social Event Prediction

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.