早期诊断艾滋病毒病例,通过在临床笔记上的文本挖掘和机器学习模型
Rodrigo Morales-Sánchez1, Soto Montalvo2, Adrián Riaño2
1Dept. of Lenguajes y Sistemas Informáticos, Escuela Técnica Superior de Ingeniería Informática, Universidad Nacional de Educación a Distancia (UNED), Juan del Rosal 16, Madrid, 28040, Spain.
Computers in biology and medicine
|July 11, 2024
概括
早期的艾滋病毒诊断至关重要. 大型语言模型 (LLM) 通过使用临床笔记有效地识别可疑的人类免疫缺陷病毒 (HIV) 病例,优于传统方法并有助于及时诊断.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 公共卫生 公共卫生
背景情况:
- 未被诊断的人类免疫缺陷病毒 (HIV) 感染导致发病率和传播增加.
- 尽量减少在医疗保健机构中错过的艾滋病毒诊断机会,对于疫情控制至关重要.
- 目前用于HIV诊断的机器学习 (ML) 方法主要使用结构化数据,忽视非结构化的电子健康记录 (EHR).
研究的目的:
- 调查仅使用非结构化的临床笔记来分类疑似艾滋病毒患者或未疑似艾滋病毒患者的有效性.
- 将经典ML算法的性能与用于检测艾滋病毒怀疑的大型语言模型 (LLM) 的性能进行比较.
- 在平衡和现实世界不平衡的患者数据集上评估LLM绩效.
主要方法:
- 编制了一个来自医院的真实临床笔记数据集,将患者分类为艾滋病毒嫌疑人或非嫌疑人.
- 评估了基于非结构化文本的患者分类的经典ML算法.
- 评估了两种西班牙生物医学大语言模型 (LLMs),以评估它们从临床笔记中识别可疑艾滋病毒患者的能力.
主要成果:
- 这两种LLM在识别疑似艾滋病毒病例方面显著优于经典ML算法.
- 在不平衡的 (现实世界) 数据集上,LLMs获得了94.7%的F1得分.
- 在平衡数据集上,RoBERTa_Bio模型获得了最高的F1分数95.7%.
结论:
- 通过LLM利用非结构化临床文本显示出改善艾滋病毒诊断率的重大前景.
- 基于LLM的工具可以帮助减少在患者咨询期间错过的HIV检测机会.
- 这种方法为临床医生在识别可能需要血清学测试的患者方面提供了潜在的帮助.
关键词:
自动选自动化选电子健康记录 (EHR) 是一种电子健康记录.艾滋病病毒 艾滋病病毒 艾滋病病毒大型语言模型 (LLM)机器学习 (ML) 是指机器学习.文本挖掘 (Text Mining) 是一种文字挖掘方式.更多相关视频
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