生成型人工智能的应用,利用非结构化临床数据加速炎症性肠病研究
Alex Z Kadhim1, Zachary Green2, Iman Nazari3
1Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton SO16 6YD, UK; National Institute for Health Research (NIHR) Southampton Biomedical Research Centre, Southampton SO16 6YD, UK.
大型语言模型 (LLM) 可以有效地构建炎症性肠病 (IBD) 电子健康记录,将非结构化的文本转换为标准化数据. 这种人工智能驱动的方法增强了IBD研究的数据标准化.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 胃肠病学研究 胃肠病学研究
背景情况:
- 炎症性肠病 (IBD) 研究产生了大量的电子健康记录 (EHR) 数据.
- 构建非结构化的电子健康记录的传统方法是低效的,并且无法扩展.
- 大型语言模型 (LLM) 对于IBD数据标准化的实用性尚未被探索.
研究的目的:
- 评估LLM用于结构IBD患者的自由文本组织学和放射学报告.
- 为了比较LLM的表现与手动的临床医生策划.
- 评估微调和检索增强生成 (RAG) 对LLM绩效的影响.
主要方法:
- 开发了一个IBD特定的LLM框架,使用快速工程和微调.
- 处理了来自两个独立网站的120份自由文本报告,使用了各种LLM.
- 手动策划的报告用于性能比较.
主要成果:
- 拉玛 3.3 在从组织学和成像报告中提取IBD发现和解剖区域方面表现出卓越的性能.
- 微调增强了较小的LLM用于成像报告的性能,减少了计算需求.
- LLM成功地将非结构化报告数据转换为标准化格式.
结论:
- 基于LLM的IBD医疗记录的自动结构是可行的.
- 非结构化的IBD数据可以可靠地转化为标准化的实体学.
- 这些进步支持可扩展的,符合隐私的AI解决方案,用于医疗数据标准化.
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