一个多阶段的大型语言模型框架,用于提取与自杀相关的健康社会决定因素
Song Wang1, Yishu Wei2, Haotian Ma2
1Cockrell School of Engineering, The University of Texas at Austin, Austin, TX, USA.
这项研究引入了一种新的框架,用于提取与自杀有关的健康社会决定因素 (SDoH) 因素. 该方法提高了准确性和透明度,有助于早期风险识别和预防策略.
科学领域:
- 自然语言处理自然语言处理.
- 公共卫生信息学 公共卫生信息学
- 计算社会科学 计算社会科学
背景情况:
- 了解健康的社会决定因素 (SDoH) 对预防自杀至关重要.
- 挑战包括数据的复杂性,确定关键压力因素,以及模型解释能力有限.
研究的目的:
- 开发和评估一个多阶段的大型语言模型框架,以改进从非结构化文本中提取SDoH因子.
- 将框架的性能与现有的最先进模型进行比较.
- 评估模型解释对人类注释效率和准确性的影响.
主要方法:
- 为SDoH因子提取开发了一个多阶段的大型语言模型框架.
- 使用预训练的BioBERT,GPT-3.5-turbo和DeepSeek-R1.1进行比较分析.
- 通过自动比较和对注释辅助的试点用户研究进行评估.
主要成果:
- 拟议的框架显著改善了SDoH因素提取和上下文检索.
- 微调更小的,针对特定任务的模型,以更低的成本产生了可比或更好的性能.
- 多阶段设计通过中间解释提高了可解释性.
结论:
- 该框架提高了提取与自杀有关的SDoH的准确性和透明度.
- 这一进步支持更早地识别有风险的个体.
- 改进的数据分析可以为更有效的自杀预防策略提供信息.
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