基于子群学习的可解释建模框架:糖尿病肾病预测研究。

IF 2.2 3区 医学 Q2 HEALTH CARE SCIENCES & SERVICES
Bo Liu, Xiangzhou Zhang, Kang Liu, Xinhou Hu, Eric W T Ngai, Weiqi Chen, Ho Yin Chan, Yong Hu, Mei Liu
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引用次数: 0

摘要

目的:糖尿病肾病(DKD)等复杂疾病通常具有异质性,这对机器学习的准确风险预测提出了挑战。传统的全局模型忽略了患者的差异,亚组学习缺乏可解释性和预测效率。方法:iSLIM 将专家知识与基于树的递归分区方法相结合,在包含 11559 名患者的电子病历数据集中识别 DKD 亚组。然后,它为每个亚组构建单独的模型,在保持可解释性的同时提高预测准确性:结果:确定了五个临床相关亚组,平均灵敏度为 0.8074,比单一全局模型高出 0.1104。事后分析提供了支持亚组有效性和潜在 DKD 风险因素的病理和生物学证据:iSLIM在预测性能和亚组特异性风险因素解释方面超越了传统的全局模型,增强了对DKD异质性机制的理解,并有可能在临床决策中更多地采用机器学习模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Interpretable subgroup learning-based modeling framework: Study of diabetic kidney disease prediction.

Objectives: Complex diseases, like diabetic kidney disease (DKD), often exhibit heterogeneity, challenging accurate risk prediction with machine learning. Traditional global models ignore patient differences, and subgroup learning lacks interpretability and predictive efficiency. This study introduces the Interpretable Subgroup Learning-based Modeling (iSLIM) framework to address these issues.

Methods: iSLIM integrates expert knowledge with a tree-based recursive partitioning approach to identify DKD subgroups within an EHR dataset of 11,559 patients. It then constructs separate models for each subgroup, enhancing predictive accuracy while preserving interpretability.

Results: Five clinically relevant subgroups are identified, achieving an average sensitivity of 0.8074, outperforming a single global model by 0.1104. Post hoc analyses provide pathological and biological evidence supporting subgroup validity and potential DKD risk factors.

Conclusion: The iSLIM surpasses traditional global model in predictive performance and subgroup-specific risk factor interpretation, enhancing the understanding of DKD's heterogeneous mechanisms and potentially increasing the adoption of machine learning models in clinical decision-making.

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来源期刊
Health Informatics Journal
Health Informatics Journal HEALTH CARE SCIENCES & SERVICES-MEDICAL INFORMATICS
CiteScore
7.80
自引率
6.70%
发文量
80
审稿时长
6 months
期刊介绍: Health Informatics Journal is an international peer-reviewed journal. All papers submitted to Health Informatics Journal are subject to peer review by members of a carefully appointed editorial board. The journal operates a conventional single-blind reviewing policy in which the reviewer’s name is always concealed from the submitting author.
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