# Characterizing gastrointestinal disorder trajectories for autistic sub-groups: Machine learning prediction of risk profiles and response to treatment

> **NIH NIH R01** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2024 · $456,133

## Abstract

PROJECT SUMMARY/ABSTRACT
Gastrointestinal (GI) problems are one of the most common concerns reported by families of autistic children
and youth and can have significant lifelong impacts on health, quality of life, and participation. Existing
research, however, primarily relies on parent report and is cross-sectional, leaving a gap in our understanding
of GI trajectories, i.e., how the symptoms emerge, including clinical and behavioral indicators; and how they
develop (and potentially change) over time. Further, existing research lacks standard approaches to measuring
GI symptoms; and a dearth of research using large, diverse, real-world clinical datasets limits our
understanding of which sub-groups are at higher risk of which constellation of symptoms, and what factors
predict response to standard of care treatments. The proposed multimethod research addresses these gaps by
using qualitative, participatory, and machine learning approaches to build prediction models of risk of GI
symptom profiles and response to treatment among autistic sub-groups. Our study aims to: (1) Qualitatively
describe autistic people’s GI experiences throughout the lifespan through analysis of narrative interviews with
25 autistic adults and 25 caregivers of autistic children/youth. (2) Quantitatively characterize GI symptom rates,
presentations, trajectories, and responses to treatment using electronic health records (EHRs) from Children’s
Hospital Los Angeles (CHLA) (N=7,478 autistic children/youth ages 1 to 25) with both (a) structured data (e.g.,
diagnosis codes, prescriptions) and (b) unstructured data (i.e., keywords extracted from clinical notes via
natural language processing). (3) Build predictive models of risk of GI symptom profiles and response to
treatment using both traditional and machine learning approaches with the Aim 2 dataset and a matched
cohort of non-autistic children and youth (1:5). To ground our work in lived experience perspectives, and in
response to autism community advocacy for autistic representation in research, we will use a participatory
research approach with a community advisory board made up of (a) autistic adults and (b) caregivers of
autistic children and youth, who will contribute to data collection, analysis, interpretation, and dissemination.
The proposed study has the strong potential to contribute to a personalized medicine approach to GI disorders
for autistic people, including targeted risk assessments across the lifespan, to improve effective, person-
centered care.

## Key facts

- **NIH application ID:** 10946338
- **Project number:** 1R01HD115661-01
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** Amber M. Angell
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $456,133
- **Award type:** 1
- **Project period:** 2024-08-01 → 2029-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10946338

## Citation

> US National Institutes of Health, RePORTER application 10946338, Characterizing gastrointestinal disorder trajectories for autistic sub-groups: Machine learning prediction of risk profiles and response to treatment (1R01HD115661-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10946338. Licensed CC0.

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