1. Deep learning for identifying personal and family history of suicidal thoughts and behaviors from EHRs.
- Author
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Adekkanattu, Prakash, Furmanchuk, Al'ona, Wu, Yonghui, Pathak, Aman, Patra, Braja Gopal, Bost, Sarah, Morrow, Destinee, Wang, Grace Hsin-Min, Yang, Yuyang, Forrest, Noah James, Luo, Yuan, Walunas, Theresa L., Lo-Ciganic, Weihsuan, Gelad, Walid, Bian, Jiang, Bao, Yuhua, Weiner, Mark, Oslin, David, and Pathak, Jyotishman
- Subjects
SUICIDE risk factors ,RISK assessment ,STATISTICAL correlation ,SUICIDAL ideation ,ACADEMIC medical centers ,RESEARCH funding ,EVALUATION of human services programs ,STATISTICAL sampling ,NATURAL language processing ,FAMILY history (Medicine) ,DESCRIPTIVE statistics ,LONGITUDINAL method ,DEEP learning ,ELECTRONIC health records ,STATISTICS ,SOFTWARE architecture ,SOCIODEMOGRAPHIC factors ,NOSOLOGY - Abstract
Personal and family history of suicidal thoughts and behaviors (PSH and FSH, respectively) are significant risk factors associated with suicides. Research is limited in automatic identification of such data from clinical notes in Electronic Health Records. This study developed deep learning (DL) tools utilizing transformer models (Bio_ClinicalBERT and GatorTron) to detect PSH and FSH in clinical notes derived from three academic medical centers, and compared their performance with a rule-based natural language processing tool. For detecting PSH, the rule-based approach obtained an F1-score of 0.75 ± 0.07, while the Bio_ClinicalBERT and GatorTron DL tools scored 0.83 ± 0.09 and 0.84 ± 0.07, respectively. For detecting FSH, the rule-based approach achieved an F1-score of 0.69 ± 0.11, compared to 0.89 ± 0.10 for Bio_ClinicalBERT and 0.92 ± 0.07 for GatorTron. Across sites, the DL tools identified more than 80% of patients at elevated risk for suicide who remain undiagnosed and untreated. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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