# Modeling Temporality with Natural Language Processing to Predict Readmission Risk of Patients with Psychosis

> **NIH NIH R01** · BOSTON CHILDREN'S HOSPITAL · 2022 · $714,531

## Abstract

Project Summary
A substantial proportion of psychiatric inpatients are readmitted within 30 days of discharge. Readmissions not
only are disruptive but also cause enormous economic burden for patients and families, and are a key driver of
rising healthcare costs. Reducing and predicting unplanned readmission are therefore major unmet needs of
psychiatric care. Developing machine learning (ML)-based natural language processing (NLP) prediction tools
using electronic health records (EHRs) is a key priority as such tools could not only be used to help target the
delivery of resource-intensive interventions to those patients at greatest risk, but also reduce psychiatric health-
care costs. A key aspect in building effective risk predictive models is the modeling of temporal structure in the
narratives. Information about the historical and present health states and timing of events (e.g., substance use
start/stop timing, recent fluctuations in suicidality or symptoms), may play a key role in predicting readmission
risk. Natural language annotation (i.e., tagging text such as events, symptoms, and anchoring them on a timeline)
is a key step for training ML classifiers. No psychiatry-specific resources or guidelines exist for the modeling of
temporality in clinical text, and as a result no robust scalable and explainable ML predictive models incorporating
temporal information have been developed.
We propose to deliver a psychiatric specific temporal relation annotation scheme, build open-source tools for
extracting temporal information, and develop readmission prediction models for psychiatric patients. Aim 1 is a
data resource creation aim in which we create a large repository of psychiatric text for building our readmission
classifier, de-identify a subset of that data to allow for sharing with the research community, and create a layer
of temporal annotations for that subset. In Aim 2, we extract temporal information from the data in the repository
to create temporal graphs, and apply graph neural networks to these graphs to extract features for predicting
30-day readmission risk. In Aim 3 we build and evaluate multiple versions of 30-day readmission risk classifiers,
and feedback performance to Aim 2 to improve temporal modeling. We develop unsupervised clustering on top
of our classifiers to discover patient sub-groups. We include practical evaluations including a comparison to
human experts and an evaluation of model performance on simulated future data. The study brings together a
team experienced in psychiatric phenotyping and application of EHRs, and a team active in developing cutting-
edge methods in ML for natural language data. This work will serve as the foundation for future translational
studies, including implementing readmission classifiers into clinical workflows and clinical trials of interventions
to reduce readmission risk.

## Key facts

- **NIH application ID:** 10445583
- **Project number:** 1R01MH126977-01A1
- **Recipient organization:** BOSTON CHILDREN'S HOSPITAL
- **Principal Investigator:** Mei-Hua Hall
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $714,531
- **Award type:** 1
- **Project period:** 2022-08-01 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10445583

## Citation

> US National Institutes of Health, RePORTER application 10445583, Modeling Temporality with Natural Language Processing to Predict Readmission Risk of Patients with Psychosis (1R01MH126977-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10445583. Licensed CC0.

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