Suicide Phenotyping from Clinical Notes in Safety-Net Psychiatric Hospital Using Multi-Label Classification with Pre-Trained Language Models

Authors

Zehan Li
Yan Hu
Scott Lane
Salih Selek
Lokesh Shahani
Rodrigo Machado-Vieira
Jair Soares
Hua Xu
Hongfang Liu
Ming Huang

Language

English

Publication Date

1-1-2025

Journal

AMIA Joint Summits on Translational Science Proceedings

PMID

40502237

PMCID

PMC12150747

PubMedCentral® Posted Date

6-10-2025

PubMedCentral® Full Text Version

Post-print

Abstract

Accurate identification and categorization of suicidal events can yield better suicide precautions, reducing operational burden, and improving care quality in high-acuity psychiatric settings. Pre-trained language models offer promise for identifying suicidality from unstructured clinical narratives. We evaluated the performance of four BERT-based models using two fine-tuning strategies (multiple single-label and single multi-label) for detecting coexisting suicidal events from 500 annotated psychiatric evaluation notes. The notes were labeled for suicidal ideation (SI), suicide attempts (SA), exposure to suicide (ES), and non-suicidal self-injury (NSSI). RoBERTa outperformed other models using binary relevance (acc=0.86, F1=0.78). MentalBERT (F1=0.74) also exceeded BioClinicalBERT (F1=0.72). RoBERTa fine-tuned with a single multi-label classifier further improved performance (acc=0.88, F1=0.81), highlighting that models pre-trained on domain-relevant data and the single multi-label classification strategy enhance efficiency and performance.

Published Open-Access

yes

Recommended Citation

Li, Zehan; Hu, Yan; Lane, Scott; et al., "Suicide Phenotyping from Clinical Notes in Safety-Net Psychiatric Hospital Using Multi-Label Classification with Pre-Trained Language Models" (2025). Faculty, Staff and Student Publications. 3299.
https://digitalcommons.library.tmc.edu/uthmed_docs/3299