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使用机器学习方法识别了四个新的急性-慢性肝功能衰竭集群,具有不同的临床轨迹和死亡率

Nipun Verma¹, Pratibha Garg¹, Arun Valsan¹

¹Department of Hepatology, Post Graduate Institute of Medical Education and Research, Chandigarh, India.

Alimentary pharmacology & therapeutics

|September 23, 2024

概括

机器学习识别了急性-慢性肝衰竭 (ACLF) 的不同患者群,揭示了独特的轨迹和生存结果. 这种分层有助于理解ACLF异质性,并指导患者管理.

科学领域:

肝病学肝病学是一种肝病学.
计算生物学计算生物学
数据科学数据科学数据科学

背景情况:

急性至慢性肝衰竭 (ACLF) 是一种具有复杂潜在模式的异质性疾病.
在ACLF中确定不同的患者子组对于了解疾病进展和结果至关重要.

研究的目的:

采用机器学习 (ML) 来识别和描述ACLF中以前未知的患者群.
使用ML模型预测这些集群并评估它们的临床意义.

主要方法:

利用了来自1568名ACLF患者的临床数据.
应用了基于距离,密度和模型的集群算法,选择了潜在类模型 (LCM) 以其可解释性.
训练有素的监督ML模型用于集群预测和执行时间验证.

主要成果:

LCM确定了四个不同的ACLF集群,它们的轨迹,可逆性和生存率各不相同,独立于现有的评分系统.
循环和功能衰竭显著影响了集群分配.
将这些集群纳入其中,使CLIF-C ACLF得分的分辨准确度提高了11%.

结论:

机器学习成功地识别了具有明显表型,轨迹和结果的新型ACLF集群.
这种ML驱动的分层提供了一个强大的工具来解决ACLF异质性,改善预后,并指导临床决策.

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