可解释的机器学习模型用于预测康复期COVID-19患者的肺扩散能力受损
Fu-Qiang Ma1, Cong He2,3,4, Hao-Ran Yang5
1Hubei University of Chinese Medicine, Wuhan, 430065, China.
BMC medical informatics and decision making
|August 29, 2023
概括
这项研究开发了一种机器学习模型,用于预测COVID-19幸存者的肺扩散能力受损. XGBoost模型准确地确定了血红蛋白和最大自愿通风等关键临床因素,用于预测长期的肺功能.
科学领域:
- 肺部医学 肺部医学
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- COVID-19幸存者经常经历肺扩散能力障碍 (PDCI).
- 预测PDCI对于评估COVID-19幸存者的长期肺功能至关重要.
- 目前在这个人群中PDCI的预测模型有限.
研究的目的:
- 开发和验证一种机器学习 (ML) 模型,用于预测COVID-19康复期患者的PDCI.
- 利用常规可用的临床数据进行PDCI预测.
- 帮助临床诊断和管理COVID-19后的肺部并发症.
主要方法:
- 一组221名COVID-19幸存者在出院18个月后进行了研究.
- 数据被随机分为培训 (80%) 和验证 (20%) 组.
- 评估了6个ML模型,使用特征选择和数据平衡技术.
主要成果:
- XGBoost模型表现出最佳性能,AUC为0.755,准确度为78.01%.
- 血红蛋白 (Hb),最大自发呼吸 (MVV),疾病严重程度,血小板计数 (PLT),尿酸 (UA) 和血尿素 (BUN) 被确定为关键预测因素.
- 根据SHAP的分析,Hb和MVV是影响力最大的因素.
结论:
- 开发的XGBoost模型显示了COVID-19幸存者中PDCI的强有力的预后能力.
- 临床因素,特别是Hb和MVV,是COVID-19后长期肺功能的重要预测因素.
- 这种ML方法可以帮助临床医生识别患PDCI风险的幸存者.
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