# Exploratory Research Project - ADAPT

> **NIH NIH P50** · UNIV OF MASSACHUSETTS MED SCH WORCESTER · 2024 · $242,538

## Abstract

ADAPT (EXPLORATORY PROJECT): SUMMARY/ABSTRACT
 Significance: Machine learning-based risk algorithms have transformational potential to improve suicide risk
identification. However, the lack of large-scale validations, transfer guidance, and automated learning-based
adaptation impedes adoption in clinical practice. This project aims to address this translation gap by
systematically assessing and improving a suicide risk algorithm’s generalizability and adaptability from an
original development setting to a new healthcare system.
 Investigators: The transdisciplinary team has comprehensive expertise in applying advanced machine
learning techniques on electronic health record (EHR) data for predictive modeling and prevention analytics (Liu,
Aseltine, Simon), studying clinical diagnosis, prognosis and treatment of serious mood disorders and suicide
(Rothschild), identifying and assessing suicide risk (Simon), and promoting health services delivery redesign
through technology and implementing informatics tools in clinical settings (Gerber).
 Innovation: This pioneering study will comprehensively evaluate and improve the generalizability and
adaptability of an evidence-based suicide risk algorithm in different contexts. The team will build a unified pipeline
of Automated, Data-driven, AdaPtable, and Transferable learning for suicide risk prediction (ADAPT). The
versatile ADAPT tool will be accessible to non-expert users and compatible with EHR common data model
standards, providing a scalable, interpretable and sustainable solution to risk algorithm translation across
different clinical contexts. Moreover, we will design an advanced deep learning approach for suicide risk
prediction and evaluate its effectiveness on generalizability and adaptability.
 Approach: The proposed study aims to assess the generalizability of the Mental Health Research Network
(MHRN) risk algorithm and explore transfer and ensemble learning to adapt a previously learned model from
original data sources into a tailored one optimized for a new health system (Aim 1); develop a unified pipeline,
ADAPT, to integrate data preprocessing, model assessment and adaptation, model interpretation, and
automated learning; explore how ADAPT’s results can be used to help match individuals to a range of
intervention approaches where specialized or intensive treatment is reserved for those with the highest risk
(Aim 2); design an innovative deep learning approach and test its effectiveness using ADAPT (Aim 3a); engage
stakeholders to better understand potential barriers and facilitators to implementation, iteratively improve
ADAPT’s usability, acceptability, and feasibility through their feedback using validated scales (Aim 3b).
 Environment: The UMass Chan Medical School (UMass) has proven its ability to support this ambitious
study by its success with numerous NIMH-funded systems-based suicide prevention studies.
 Impact: The study holds great potential for promoting the implementation of an ...

## Key facts

- **NIH application ID:** 10821343
- **Project number:** 5P50MH129701-02
- **Recipient organization:** UNIV OF MASSACHUSETTS MED SCH WORCESTER
- **Principal Investigator:** Feifan Liu
- **Activity code:** P50 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $242,538
- **Award type:** 5
- **Project period:** 2023-04-05 → 2028-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10821343

## Citation

> US National Institutes of Health, RePORTER application 10821343, Exploratory Research Project - ADAPT (5P50MH129701-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10821343. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*