Suicide Tendency Prediction from Psychiatric Notes Using Transformer Models

Zehan Li, Iqra Ameer, Yan Hu, Ahmed Abdelhameed, Cui Tao, Salih Selek, Hua Xu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Suicide tendency is a fluid and multifaceted process that involves various stages, including suicidal ideation, planning, and attempting suicide. The use of electronic health records (EHR) and predictive algorithms has provided unprecedented opportunities for suicide research, but standard diagnosis codes for suicide tendencies are not always readily available in health records, resulting in low sensitivity when identifying suicide tendencies using structured data. Prior studies have focused on developing binary classification models to identify the presence of single suicide tendencies, such as suicide ideation or suicide attempt. In this study, we have worked on multiclass suicide tendency problem. We conducted a series of experiments to predict multiple suicide tendencies from psychiatric evaluation notes using classic machine learning models and pretrained transformer models. We manually annotated 1,000 Initial Psychiatric Evaluation (IPE) notes using a set of three classes (suicide ideation, suicide attempt, and non-suicidal). The performance of these models were evaluated using weighted F1 score, precision, recall, and accuracy. The Bio-ClinicalBERT model achieved the best performance for multiclass classification, with a weighted F1 score of 0.78, outperforming the classic machine learning models. Logistic regression and random forest models achieved comparable performance to state-of-the-art models in binary classification tasks with F1 score and accuracy of 0.93. The study contributes to mental health informatics with a novel Natural Language Paper (NLP) approach and psychiatric dataset.

Original languageEnglish (US)
Title of host publicationProceedings - 2023 IEEE 11th International Conference on Healthcare Informatics, ICHI 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages481-483
Number of pages3
ISBN (Electronic)9798350302639
DOIs
StatePublished - 2023
Event11th IEEE International Conference on Healthcare Informatics, ICHI 2023 - Houston, United States
Duration: Jun 26 2023Jun 29 2023

Publication series

NameProceedings - 2023 IEEE 11th International Conference on Healthcare Informatics, ICHI 2023

Conference

Conference11th IEEE International Conference on Healthcare Informatics, ICHI 2023
Country/TerritoryUnited States
CityHouston
Period6/26/236/29/23

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Health Informatics

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