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

Publication Date

1-1-2025

Journal

AMIA Joint Summits on Translational Science Proceedings

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.

PMID

40502237

PMCID

PMC12150747

PubMedCentral® Posted Date

6-10-2025

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes