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急性中风CDS:从非结构化的临床信件中自动检索血栓溶解禁忌
Murray Cutforth1, Hannah Watson1, Cameron Brown2
1Canon Medical Research Europe, Edinburgh, United Kingdom.
Frontiers in digital health
|June 30, 2023
概括
机器学习准确地检测患者的病史,以决定急性缺血性中风中的血栓溶解. 这种临床决策支持工具有助于迅速治疗,通过更快地识别禁忌,改善患者的治疗结果.
科学领域:
- 生物医学自然语言处理
- 临床决策支持系统 临床决策支持系统
- 医疗保健中的机器学习
背景情况:
- 对于急性缺血性中风的血栓溶解需要早期管理以获得最佳结果.
- 诸如最近的手术或抗凝药等禁忌,需要仔细审查患者病史.
- 手动审查患者记录是耗时的,可能会推迟关键的治疗决策.
研究的目的:
- 开发和评估一种机器学习方法,用于在非结构化临床文本中自动检测血栓溶解的禁忌.
- 支持临床医生及时做出关于急性缺血性中风血栓溶解的决定.
- 通过更快,更准确地识别相关病史来改善患者的治疗结果.
主要方法:
- 咨询指南,以确定86个有关血栓溶解资格的实体.
- 手动注释了2,912名患有这些实体的患者的8067份文件.
- 经过训练和验证的基于变压器的命名实体识别 (NER) 模型,包括PubMedBERT.
主要成果:
- 最好的基于PubMedBERT的模型获得了0.829/0.723.3的微/宏F1分数.
- 将5个模型变体组合在一起,性能提高到微/宏F1的0.846/0.734,接近人类注释器的性能.
- 名称规律性被认为是模型性能比训练集频率更强的预测因素.
结论:
- 机器学习,特别是NER,在关键时间中风治疗中显示出临床决策支持的巨大潜力.
- 患者病史的自动检测可以加快血栓溶解的实施,从而改善患者的治疗结果.
- 开发的模型提供了一个有前途的工具,以帮助临床医生在复杂的治疗决策.
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