开发和验证可解释的机器学习模型,用于警告与肝炎E病毒相关的急性肝衰竭
Rui Dong1, Zhenghan Luo2, Hong Xue3
1Department of Fundamental and Community Nursing, School of Nursing, Nanjing Medical University, Nanjing, China.
概括
机器学习准确地预测了E型肝炎患者急性肝衰竭风险. 一个生存梯度增强模型可以识别高风险个体,以便及时进行干预.
科学领域:
- 肝病学 肝病学是一种肝病学.
- 机器学习在医学中的应用
- 预测分析 (Predictive Analytics) 是一种分析方法.
背景情况:
- 早期识别急性肝炎E (AHE) 患者的肝炎E病毒相关急性肝衰竭 (HEV-ALF) 的高风险至关重要.
- 及时的监测和干预策略取决于准确的风险分层.
- 第三级护理机构需要强大的工具来管理AHE的进展.
研究的目的:
- 开发和验证可解释的机器学习 (ML) 模型,用于预测HEV-ALF风险.
- 在医院环境中识别高风险的AHE患者.
- 通过准确的风险预测,增强临床决策.
主要方法:
- 一个多中心的回顾性队列研究,涉及7个三级医疗中心的1912名患者.
- 应用多个ML算法用于特征选择和模型开发.
- 使用歧视,校准和临床净效益来评估预测性表现,并使用SHapley添加剂解释性.
主要成果:
- 开发了10个ML模型,使用共识选择的7个基线特征.
- 生存梯度增强机 (GBM) 模型的表现优于传统的考克斯回归和其他模型.
- 在外部验证中,GBM模型获得了Harrell的0.853的一致性指数,并作为一个Web工具部署.
结论:
- 开发的GBM模型在预测HEV-ALF风险方面表现出色.
- 这种可解释的ML工具为AHE管理中的临床决策提供了有希望的方法.
- 该模型有助于早期识别需要更密切监测和干预的患者.
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