Exploring generalizability and explainability of LLMs in classifying clinically rated suicidal ideation using heterogeneous data
TL;DR
Results showed that topic-specific classifiers reduced subgroup disparity compared to the topic-general classifier, and a factorization approach to decompose complex data into simpler components by reducing topic dimensions of clinical transcripts was proposed.
Abstract
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.
