An interpretable imbalance ensemble classification method for readmission risk assessment incorporating multi-view perturbation and SHAP analysis

IF 6.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Shaoze Cui , Ruize Gao , Junwei Kuang , Liang Yang , Huaxin Qiu , Xiaowen Wei
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引用次数: 0

Abstract

In the domain of medical services, patients are frequently readmitted shortly after discharge due to inadequate discharge planning or relapses of their illnesses. Such occurrences not only deplete valuable medical resources but also compromise patient satisfaction with the medical care they receive. To address this issue, we propose an interpretable imbalance ensemble classification method incorporating multi-view perturbation to evaluate the risk of patient readmission. Our study introduces a novel multi-view perturbation technique to bolster the model's generalization capabilities. Furthermore, we propose a more robust ensemble strategy based on Evidential Reasoning (EVR) rules, which enhances the stability of the ensemble learning model's fusion outcomes. Additionally, recognizing the impact of sensitive parameters on model performance, we present a parameter optimization approach utilizing the Differential Evolution (DE) algorithm, which balances model predictive accuracy and computational efficiency within the fitness function. Empirical results using real-world medical data indicate that our proposed method accurately identifies patients at high risk of readmission and surpasses current state-of-the-art methods in risk assessment.
多视角摄动与SHAP分析相结合的可解释的再入院风险综合分类方法
在医疗服务领域,由于出院计划不充分或病情复发,病人往往在出院后不久就再次入院。这种情况不仅耗尽了宝贵的医疗资源,而且降低了患者对所接受的医疗服务的满意度。为了解决这个问题,我们提出了一种可解释的不平衡集合分类方法,结合多视图扰动来评估患者再入院的风险。我们的研究引入了一种新的多视图摄动技术来增强模型的泛化能力。此外,我们提出了一种基于证据推理(EVR)规则的鲁棒集成策略,增强了集成学习模型融合结果的稳定性。此外,认识到敏感参数对模型性能的影响,我们提出了一种利用差分进化(DE)算法的参数优化方法,该方法在适应度函数内平衡模型预测精度和计算效率。使用真实医疗数据的实证结果表明,我们提出的方法准确地识别了再入院高风险的患者,并且在风险评估方面超过了目前最先进的方法。
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来源期刊
Decision Support Systems
Decision Support Systems 工程技术-计算机:人工智能
CiteScore
14.70
自引率
6.70%
发文量
119
审稿时长
13 months
期刊介绍: The common thread of articles published in Decision Support Systems is their relevance to theoretical and technical issues in the support of enhanced decision making. The areas addressed may include foundations, functionality, interfaces, implementation, impacts, and evaluation of decision support systems (DSSs).
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