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

> **NIH HD R01** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2026 · $698,064

## 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 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 

## Key facts

- **NIH application ID:** 11318931
- **Project number:** 5R01HD115661-03
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** Amber M. Angell
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** HD
- **Fiscal year:** 2026
- **Award amount:** $698,064
- **Award type:** 5
- **Project period:** 2024-08-01T00:00:00 → 2029-04-30T00:00:00

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11318931

## Citation

> US National Institutes of Health, RePORTER application 11318931, Characterizing gastrointestinal disorder trajectories for autistic sub-groups: Machine learning prediction of risk profiles and response to treatment (5R01HD115661-03). Retrieved via AI Analytics 2026-07-09 from https://api.ai-analytics.org/grant/nih/11318931. Licensed CC0.

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