A Multi-Stage Large Language Model Framework for Extracting Suicide-Related Social Determinants of Health
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915433298264064 |
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| author | Wang, Song Wei, Yishu Ma, Haotian Lovitt, Max Deng, Kelly Meng, Yuan Xu, Zihan Zhang, Jingze Xiao, Yunyu Ding, Ying Xu, Xuhai Ghosh, Joydeep Peng, Yifan |
| author_facet | Wang, Song Wei, Yishu Ma, Haotian Lovitt, Max Deng, Kelly Meng, Yuan Xu, Zihan Zhang, Jingze Xiao, Yunyu Ding, Ying Xu, Xuhai Ghosh, Joydeep Peng, Yifan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_05003 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Multi-Stage Large Language Model Framework for Extracting Suicide-Related Social Determinants of Health Wang, Song Wei, Yishu Ma, Haotian Lovitt, Max Deng, Kelly Meng, Yuan Xu, Zihan Zhang, Jingze Xiao, Yunyu Ding, Ying Xu, Xuhai Ghosh, Joydeep Peng, Yifan Computation and Language Artificial Intelligence 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. |
| title | A Multi-Stage Large Language Model Framework for Extracting Suicide-Related Social Determinants of Health |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2508.05003 |