# Person-centered diagnostics and prediction for child dysregulatory psychopathology using novel phenotypes

> **NIH NIH U01** · OREGON HEALTH & SCIENCE UNIVERSITY · 2024 · $2,625,977

## Abstract

Project Summary
This U01 proposal is submitted in response to the NIMH IMPACT RFA (RFA-MH-23-105). It will extend research
efforts to both define novel, deployable behavioral phenotypes (highlighting cognitive and emotional
transdiagnostic measures) and develop and test diagnostic and prognostic prediction models, using advanced
analytics, within the clinic setting. It does so for children aged 7-17, presenting with common forms of
psychopathology, characterized by dysregulation of attention, behavior, and/or emotion. Aim 1 uses established
longitudinal research cohorts to further refine computational phenotypes for cognition (executive functions,
alertness/arousal) and low-cost trait ratings relevant to emotional regulation and valence, and in combination
with key environmental variables, tests their cross-sectional and longitudinal predictive utility using machine
learning models. Aim 2 uses large electronic medical record (EMR) data to refine sophisticated neural network
models to enhance mental health diagnostics and outcome prediction in children. Aims 3 and 4 entail new data
collection of several thousand patients in four pediatric and psychiatry clinic sites around the country, and then
combines and extends findings from Aims 1 and 2 to test their diagnostic and prognostic effectiveness in these
diverse patient populations. The study is significant in its potential to open the way for clinical care to benefit
from years of scientific progress in phenotype refinement that are low cost and deployable. It is further significant
in its potential to harvest from existing EMR data far more clinically useful prediction algorithms than are currently
available. The inclusion of sequential Bayesian logic for aiding clinicians in deciding which cases require
additional assessment and which do not will be transformative in opening a path for significant savings in cost
and clinician time by improving efficiency of care. The ability to better predict critical outcomes, such as
worsening of mental health symptoms, suicidality, or increased resource utilization is urgent and will be
addressed in our work. This project is innovative in combining large machine learning models from EMR data
with similar models using novel research phenotypes and will be the first prospective test of these models in
patients recruited from active clinics in multiple locations to evaluate generalizability. Further significance and
innovation are added by careful attention to the role of environmental adversity and extensive plans to minimize
or overcome the asynchronous benefit of such efforts to historically under-served and under-represented
populations. The project directly, significantly, and with innovation addresses the goals and purpose of the
IMPACT RFA by aiming to demonstrate how novel behavioral phenotypes can enhance clinical care even as
maximum value is extracted from EMR data already in hand.

## Key facts

- **NIH application ID:** 10864716
- **Project number:** 1U01MH135970-01
- **Recipient organization:** OREGON HEALTH & SCIENCE UNIVERSITY
- **Principal Investigator:** JOEL T NIGG
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $2,625,977
- **Award type:** 1
- **Project period:** 2024-05-15 → 2029-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10864716

## Citation

> US National Institutes of Health, RePORTER application 10864716, Person-centered diagnostics and prediction for child dysregulatory psychopathology using novel phenotypes (1U01MH135970-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10864716. Licensed CC0.

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