Kavli Affiliate: Wei Gao
| First 5 Authors: Xinzhe Zheng, Sijie Ji, Jiawei Sun, Renqi Chen, Wei Gao
| Summary:
Mental health risk is a critical global public health challenge,
necessitating innovative and reliable assessment methods. With the development
of large language models (LLMs), they stand out to be a promising tool for
explainable mental health care applications. Nevertheless, existing approaches
predominantly rely on subjective textual mental records, which can be distorted
by inherent mental uncertainties, leading to inconsistent and unreliable
predictions. To address these limitations, this paper introduces ProMind-LLM.
We investigate an innovative approach integrating objective behavior data as
complementary information alongside subjective mental records for robust mental
health risk assessment. Specifically, ProMind-LLM incorporates a comprehensive
pipeline that includes domain-specific pretraining to tailor the LLM for mental
health contexts, a self-refine mechanism to optimize the processing of
numerical behavioral data, and causal chain-of-thought reasoning to enhance the
reliability and interpretability of its predictions. Evaluations of two
real-world datasets, PMData and Globem, demonstrate the effectiveness of our
proposed methods, achieving substantial improvements over general LLMs. We
anticipate that ProMind-LLM will pave the way for more dependable,
interpretable, and scalable mental health case solutions.
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