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Background <p>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.</p> Methods <p>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.</p> Results <p>We show that our proposed framework demonstrates 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.</p> Conclusions <p>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.</p>

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A multi-stage large language model framework for extracting suicide-related social determinants of health

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月29日

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  • 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

摘要

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 demonstrates 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.