一个可解释的机器学习模型用于预测慢性冠状动脉疾病和识别有价值的文本特征.
Weipeng Gan1, Peipei Wang2, Xiangrong Xie3
1Department of Cardiovascular Medicine, The Second Affiliated Hospital of Wannan Medical College, Wuhu, Anhui, China.
Frontiers in cardiovascular medicine
|October 8, 2025
概括
机器学习模型有效地使用可访问的文本数据和患者特征预测慢性冠状动脉疾病 (CCD). 这种方法通过识别胸痛等关键特征来提高诊断准确度.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 慢性冠状动脉疾病 (CCD) 构成了全球重大健康负担.
- 当前的前测试概率 (PTP) 模型通常依赖于医院内数据和主观的临床判断.
- 需要更容易获得和客观的方法来预测CCD.
研究的目的:
- 开发机器学习 (ML) 模型,使用易于获取的文本数据和基线患者特征来预测CCD.
- 评估文本数据对CCDML模型诊断性能的影响.
- 使用可解释性技术来解释ML模型.
主要方法:
- 应用文本挖掘来结构患者数据,包括首席投诉和病史.
- 为心血管医学开发了定制的文本处理技术.
- 随机森林模型使用患者数据进行训练和评估,并使用SHAP进行解释.
主要成果:
- 一个随机森林模型实现了0.93的AUC,这表明在预测CCD方面表现出色.
- 发现的关键预测特征包括"胸痛"和"胸紧"等文本数据,以及年龄等结构化数据.
- SHAP算法提供了关于这些特征如何影响模型预测的见解.
结论:
- 文本数据可以有效地用于构建慢性冠状动脉疾病 (CCD) 的准确预测模型.
- SHAP方法有助于临床医生了解推动CCD预测的因素.
- 这种方法为改善CCD诊断和管理提供了有价值的工具.
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