# Phenotypes REimagined to Define Clinical Treatment and Outcome Research (PREDiCTOR)

> **NIH NIH U01** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2024 · $4,170,401

## Abstract

PROJECT SUMMARY
Psychiatry faces a significant challenge in the absence of objective measures to assess behavior. Clinicians
form clinical opinions based largely on their impressions from interviewing and what they read in the electronic
health record. As a result, we are currently unable to provide reliable prognoses on an individual basis. One
untapped source of behavioral information for clinical decision-making is the clinical interview itself, which
forms the foundation of the electronic health record (EHR). Every clinical visit provides a wealth of behavioral
information comprising spoken language, eye contact, and facial expressions from both the patient and the
clinician. Another source of behavioral data, which is ecologically valid, comes from smartphones, which
provide physical activity metrics (e.g., step count, distance traveled), geolocation, social interactions (e.g., SMS
messages and phone calls made and received), sleep patterns and audio data from diaries. By analyzing
these rich behavioral datasets from routine clinical visits and smartphones, we can develop clinical signatures
for particularly clinically relevant outcomes in young help-seeking people, namely treatment disengagement,
ER visits and hospitalizations. These individualized clinical signatures are important for the real-life situation
that confronts both clinician and patient at the first visit to a mental health clinic. This proposal includes all new
patients (N = 2100), ages 15 to 30, who seek treatment for the first time at one of six outpatient mental health
clinics in the Mount Sinai Health system. Aim 1 is to create a baseline clinical signature for outcomes using
deep neural network modeling of legacy EHR data and baseline behavior, which includes audiovisual
recordings of intake interviews, ratings of working alliance, and brief surveys and tests of cognition. Aim 2 is to
use Contextual Bandit to create a longitudinal clinical signature for outcomes based on subsequent behavioral
data from clinical interviews (and their accompanying notes), and smartphone passive data and audio diary
data. Contextual Bandit is a model that keeps updating probabilities and odds over time as it is given new data.
Aim 3 is to create clinical signatures based on EHR data alone, such that the added value of behavioral data
for Aims 1 and 2 can be quantified. Study assessments are standard, low-cost, and easy to administer, with
good variance, validity, reliability, and attention to bias. Across all aims, fusion will be used for behavioral
feature extraction and natural language processing (NLP) for analysis of both written language (clinical text)
and spoken language (clinical visits and audio diaries). Data science methods have been optimized for
partnership with the DCC. Health equity, community engagement and ethical issues re privacy, informed
consent and fairness have been prioritized.

## Key facts

- **NIH application ID:** 10877461
- **Project number:** 1U01MH136535-01
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** CHERYL MARY CORCORAN
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $4,170,401
- **Award type:** 1
- **Project period:** 2024-07-11 → 2029-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10877461

## Citation

> US National Institutes of Health, RePORTER application 10877461, Phenotypes REimagined to Define Clinical Treatment and Outcome Research (PREDiCTOR) (1U01MH136535-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10877461. Licensed CC0.

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