Enhancing suicidal behavior detection in EHRs: A multi-label NLP framework with transformer models and semantic retrieval-based annotation.

Journal: Journal of biomedical informatics
PMID: 39631489

Abstract

BACKGROUND: Suicide is a leading cause of death worldwide, making early identification of suicidal behaviors crucial for clinicians. Current Natural Language Processing (NLP) approaches for identifying suicidal behaviors in Electronic Health Records (EHRs) rely on keyword searches, rule-based methods, and binary classification, which may not fully capture the complexity and spectrum of suicidal behaviors. This study aims to create a multi-class labeled dataset with annotation guidelines and develop a novel NLP approach for fine-grained, multi-label classification of suicidal behaviors, improving the efficiency of the annotation process and accuracy of the NLP methods.

Authors

  • Kimia Zandbiglari
    Department of Pharmaceutical Outcomes & Policy, University of Florida, Gainesville, FL, USA.
  • Shobhan Kumar
    Department of Pharmaceutical Outcomes & Policy, University of Florida, Gainesville, FL, USA.
  • Muhammad Bilal
    Agri-Biophotonics Division, National Institute of Lasers and Optronics (NILOP), Nilore, Islamabad 45650, Pakistan.
  • Amie Goodin
    Department of Pharmaceutical Outcomes & Policy, University of Florida, Gainesville, FL, USA.
  • Masoud Rouhizadeh
    Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

Keywords

External Resources

Popular Topics
Recent Journals