A Multi-Stage Large Language Model Framework for Extracting Suicide-Related Social Determinants of Health

• URL: http://arxiv.org/abs/2508.05003v1
• Date: Thu, 07 Aug 2025 03:36:38 GMT
• Title: A Multi-Stage Large Language Model Framework for Extracting Suicide-Related Social Determinants of Health
• Authors: Song Wang, Yishu Wei, Haotian Ma, Max Lovitt, Kelly Deng, Yuan Meng, Zihan Xu, Jingze Zhang, Yunyu Xiao, Ying Ding, Xuhai Xu, Joydeep Ghosh, Yifan Peng,
• Abstract summary: Social determinants of health (SDoH) factors contributing to suicide incidents are crucial for early intervention and prevention.<n>We present a multi-stage large language model framework to enhance SDoH factor extraction from unstructured text.
• Score: 28.066261773189865
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Background: Understanding social determinants of health (SDoH) factors contributing to suicide incidents is crucial for early intervention and prevention. However, data-driven approaches to this goal face challenges such as long-tailed factor distributions, analyzing pivotal stressors preceding suicide incidents, and limited model explainability. Methods: We present a multi-stage large language model framework to enhance SDoH factor extraction from unstructured text. Our approach was compared to other state-of-the-art language models (i.e., pre-trained BioBERT and GPT-3.5-turbo) and reasoning models (i.e., DeepSeek-R1). We also evaluated how the model's explanations help people annotate SDoH factors more quickly and accurately. The analysis included both automated comparisons and a pilot user study. Results: We show that our proposed framework demonstrated performance boosts in the overarching task of extracting SDoH factors and in the finer-grained tasks of retrieving relevant context. Additionally, we show that fine-tuning a smaller, task-specific model achieves comparable or better performance with reduced inference costs. The multi-stage design not only enhances extraction but also provides intermediate explanations, improving model explainability. Conclusions: Our approach improves both the accuracy and transparency of extracting suicide-related SDoH from unstructured texts. These advancements have the potential to support early identification of individuals at risk and inform more effective prevention strategies.

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