Exploring generalizability and explainability of LLMs in classifying clinically rated suicidal ideation using heterogeneous data

While artificial intelligence (AI) and large language models (LLMs) have shown promise in identifying and classifying suicidal ideation, their generalizability and equity in the presence of heterogeneous clinical data remain largely unexplored. This study hypothesized a subgroup disparity in a crude AI classifier of clinician-rated suicidal ideation because of the linguistic heterogeneity and proposed a factorization approach to decompose complex data into simpler components by reducing topic dimensions of clinical transcripts. Results showed that topic-specific classifiers reduced subgroup disparity compared to the topic-general classifier, with ΔAUC decreasing from 0.11 to 0.01 and 0.05—a noticeable reduction of 0.10 and 0.06, respectively.

More specifically, with the topic-general classifier, the odds of missing a suicidal case increased by 2.39 times for alexithymia individuals, compared to non-alexithymia individuals (OR = 2.39, p = 0.002). These findings underscore the significance of data heterogeneity on AI classifiers of suicidal ideation and demonstrate the potential of the proposed factorization approach. This work was supported by the Health and Medical Research Fund (09203066 and 21220821), General Research Fund (14106223), Innovation and Technology Support Programme (ITS/178/22), CUHK Direct Grant for Research (2022.073 and 2024.061), CUHK IdeaBooster Fund (IDBF24MED15), and CUHK Improvement on Competitiveness in Hiring New Faculties Funding Scheme (371).

CL was supported by the Faculty Postdoctoral Fellowship Scheme of the Chinese University of Hong Kong (FPFS/23-24/024). Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong SAR, China Rong Huang, Longdi Xian, Christopher Chi Wai Cheng, Jie Chen, Kit Ying Chan, Calvin Lam, Joey W. Chau, Ngan Yin Chan, Bei Huang, Yun Kwok Wing & Tim M.

Li Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong SAR, China Rong Huang, Longdi Xian, Christopher Chi Wai Cheng, Kit Ying Chan, Calvin Lam, Joey W. Li Department of Psychiatry, Fujian Medical University Affiliated Fuzhou Neuropsychiatric Hospital, Fuzhou, China Li Ka Shing Institute of Health Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China Gerald Choa Neuroscience Institute, The Chinese University of Hong Kong, Hong Kong SAR, China Y.K.W. received personal fees from Eisai Co. for lectures and travel support from Lundbeck HK Limited and Aculys Pharma, Japan. J.W.Y.C. received personal fee from Eisai Co., Ltd and travel support from Lundbeck HK Limited for overseas conference.

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