大型语言模型优于传统的自然语言处理方法,在炎症性肠病中提取患者报告的结果
Perseus V Patel1,2, Conner Davis3, Amariel Ralbovsky1
1Department of Pediatrics, University of California, San Francisco, San Francisco, California.
大型语言模型 (LLM) 准确地从炎症性肠病 (IBD) 临床笔记中提取患者报告的结果 (PRO). 与传统的NLP相比,LLM在各个机构中表现出更高的普遍性,增强IBD研究和护理.
科学领域:
- 自然语言处理自然语言处理.
- 医疗信息学 医疗信息学
- 胃肠病学 胃肠病学
背景情况:
- 患者报告的结果 (PROs) 对于评估炎症性肠病 (IBD) 活动和治疗有效性至关重要.
- 从临床笔记中手动提取PRO是耗时和劳动密集的.
- 改善电子健康记录 (EHR) 的数据策划对于研究和质量改进至关重要.
研究的目的:
- 为了比较传统的自然语言处理 (tNLP) 和大型语言模型 (LLMs) 来提取IBD PROs.
- 评估LLMs在从自由文本临床笔记中提取数据时的准确性和通用性.
- 评估LLM在增强IBD研究和患者护理方面的潜力.
主要方法:
- 在UCSF开发和内部测试基于tNLP和LLM的模型.
- 在斯坦福大学对模型的外部验证.
- 使用准确度,灵敏度,特异性和预测值对模型性能进行比较.
- 评估跨人口群体的模型公平性和错误模式.
主要成果:
- LLM (GPT-4) 在提取腹痛,腹和便血液的过程中取得了高精度 (>90%).
- tNLP模型最初表现良好,但未能对外部数据进行概括 (准确率为61-62%).
- 不管笔记的变化,LLMs在各个机构中保持了高准确度.
- 在LLM成绩中没有检测到人口或诊断偏差.
结论:
- 大型语言模型为从临床笔记中提取PRO提供了准确和可概括的解决方案.
- 法律学可以克服机构数据的异质性,提高研究可访问性.
- 广泛采用LLM可以显著推进IBD研究和患者管理.
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