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利用大型语言模型对异常工伤病例的早期检测:以数据为导向的方法提高康复效率
Peter Q Chen1, Hayley Y W Gu1, Heidi K Y Lo2
1Department of Computing, Hong Kong Polytechnic University, PQ710, Mong Man Wai Building, Hong Kong, China (Hong Kong), 852 27667248.
大型语言模型 (LLM) 可以自动分析工伤康复病例,以高精度识别异常. 这种方法提高了病例管理的效率和精确性,用于确定异常值以改善患者的治疗结果.
科学领域:
- 医疗保健中的人工智能
- 临床数据分析 临床数据分析
- 康复医学 康复医学 康复医学
背景情况:
- 大型语言模型 (LLM) 显示出分析非结构化临床数据的前景.
- 在劳动伤害康复治疗中使用LLM并未得到充分证实.
- 自动化分析可以提高管理工伤案件的效率.
研究的目的:
- 评估一个LLM辅助的方法来识别异常的工作伤害康复病例.
- 提高康复病例管理的可扩展性和准确性.
- 评估LLM在检测预期恢复时间偏差方面的表现.
主要方法:
- 对110,346个未被确定的工作伤害病例 (2001-2024) 的回顾性分析.
- 利用LLM来预测从自由文本伤害描述中恢复时间.
- 如果病假日超过了LLM预测的最大数,则将案件归类为异常.
主要成果:
- 在非异常分类方面,GPT-4o的准确度超过73%,在整体检测方面达到79%.
- 该LLM辅助方法在各种子组中表现出高准确度.
- 证实了从非结构化的临床笔记中可靠地提取信息.
结论:
- 在大规模的康复数据集中,LLM可以有效地自动检测异常.
- 这种方法显示了定制特定年龄的康复策略的潜力.
- 未来的研究应该针对不同医疗保健系统和地区的普遍性.
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