# JASPer-MH: Jointly Assessed Scalable Phenotypes for Mental Health

> **NIH NIH U01** · MASSACHUSETTS GENERAL HOSPITAL · 2024 · $2,142,911

## Abstract

Mental health among young adults represents a public health crisis, requiring more efficient and precise means
of understanding illness trajectories in the general population. Such models could provide opportunities for
early identification and intervention for mental illness during the period of adolescence to young adulthood, as
youth navigate key psychosocial milestones and neuropsychiatric illness becomes entrenched. Work by the
investigators and others has demonstrated the utility of electronic health records (EHR) for developing risk
stratification models for psychiatric outcomes. Yet, the investigators and others have also shown that the
diagnostic codes available in EHR are insufficient to reliably predict the evolution of psychopathology over
time. As such, RFA MH-23-105 seeks strategies to augment EHR-based models to efficiently capture
signatures that may improve clinical prediction. Among the broad range of potential measures, brief batteries
that capture dimensional traits, including quantitative symptoms and cognition, are at the core of evidence-
based, developmentally relevant psychopathology frameworks and are consistent with the consensus that
clinical staging must be approached through a transdiagnostic lens. Variation in such traits is not well-captured
by standard EHR data but represents an important aspect of neurodevelopment and its relationship to clinical
and functional outcomes. This study proposes to integrate remotely-delivered cognitive tasks and brief
symptom inventories to enhance prediction of prospective outcomes among transition age youth. Specifically, it
will apply methods developed by the investigators to a cohort of N= 10,000 individuals age 18-20 identified
from EHR in the Mass General Brigham Health Care system and assessed prospectively every 6 months for 2
years. Aim 1 will identify, enroll, and retrospectively characterize this cohort, extending EHR codes with
validated NLP methods to characterize dimensional psychiatric symptoms and cognitive functioning
retrospectively for up to 10 years, using data censored 6 or 24 months prior to baseline to predict current
status. Aim 2 will collect enhanced phenotyping (neurocognitive and self-report psychiatric and psychosocial
functioning data) on this cohort every 6 months and apply both standard and novel interpretable machine
learning methods to derive predictors of 180-day psychiatric outcomes. Aim 3 will apply interpretable machine
learning methods to determine the value of enhanced phenotyping over EHR data alone for 24-month
outcomes . We hypothesize that adding these low cost, low burden phenotypes will improve the performance
of models predicting longitudinal neuropsychiatric outcomes in a manner that can be translated across health
care systems with diverse populations.

## Key facts

- **NIH application ID:** 10867634
- **Project number:** 1U01MH136059-01
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** ALYSA E DOYLE
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $2,142,911
- **Award type:** 1
- **Project period:** 2024-05-20 → 2029-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10867634

## Citation

> US National Institutes of Health, RePORTER application 10867634, JASPer-MH: Jointly Assessed Scalable Phenotypes for Mental Health (1U01MH136059-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10867634. Licensed CC0.

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