Shi Zixin, Huang Linjun, Wang Haolin
College of Medical Informatics, Chongqing Medical University, Chongqing, China.
Digit Health. 2025 Mar 20;11:20552076251330078. doi: 10.1177/20552076251330078. eCollection 2025 Jan-Dec.
Pediatric populations are more vulnerable to drug-induced liver injury (DILI) due to distinct pharmacokinetic profiles and ongoing physiological maturation processes. However, early identification and assessment of DILI in pediatric patients present significant clinical challenges, primarily due to the inherent complexity of pediatric cases and substantial limitations in available clinical data.
This study introduces a framework that integrates clustering analysis with dynamic classifier selection (DCS) techniques to enhance pediatric DILI prediction. The proposed method addresses challenges such as patient heterogeneity and class imbalance, while optimizing predictive performance to support clinical decision-making.
We investigated a retrospective cohort of 12,555 pediatric inpatients across six hospitals in Chongqing, China. The dataset encompassed a wide range of biomedical parameters, including laboratory results and liver function profiles, along with clinical documentation spanning demographic characteristics, medical histories, and medication regimens. Patients were stratified into four distinct clinical subgroups based on silhouette coefficient. A diverse pool of base classifiers was generated with varied initialization strategies and hyperparameter optimizations tailored to each patient cluster. The classification process was further refined through the implementation of Dynamic Classifier Selection with Multiple Classifier Behavior (DCS-MCB) methodology, which adaptively customizes model selection based on the distinctive clinical profiles of each subgroup.
The Clustering-enhanced DCS-MCB framework demonstrated superior performance compared to conventional machine learning models across evaluation metrics. The ensemble learning models consistently outperformed individual classifier models, with the presented study achieving the highest F1-score (0.926), MCC (0.917), G-mean (0.959), demonstrating the strength of this hybrid approach in addressing the complexities of pediatric DILI prediction.
The integration of clustering analysis with dynamic classifier selection has demonstrated efficacy in complex real-world clinical settings. This methodology provides a more robust, precise, and clinically adaptable framework for patient stratification and drug safety surveillance.
由于独特的药代动力学特征和持续的生理成熟过程，儿科人群更容易受到药物性肝损伤（DILI）的影响。然而，儿科患者DILI的早期识别和评估面临重大临床挑战，主要是因为儿科病例本身的复杂性以及现有临床数据存在很大局限性。
本研究引入了一种将聚类分析与动态分类器选择（DCS）技术相结合的框架，以增强对儿科DILI的预测。所提出的方法解决了患者异质性和类别不平衡等挑战，同时优化预测性能以支持临床决策。
我们调查了中国重庆六家医院的12555名儿科住院患者的回顾性队列。该数据集包含广泛的生物医学参数，包括实验室检查结果和肝功能指标，以及涵盖人口统计学特征、病史和用药方案的临床记录。根据轮廓系数将患者分为四个不同的临床亚组。通过针对每个患者集群采用不同的初始化策略和超参数优化，生成了多种基础分类器。通过实施具有多分类器行为的动态分类器选择（DCS-MCB）方法进一步优化分类过程，该方法根据每个亚组的独特临床特征自适应地定制模型选择。
与传统机器学习模型相比，聚类增强的DCS-MCB框架在各项评估指标上均表现出卓越性能。集成学习模型始终优于单个分类器模型，本研究取得了最高的F1分数（0.926）、MCC（0.917）、G均值（0.959），证明了这种混合方法在应对儿科DILI预测复杂性方面的优势。
聚类分析与动态分类器选择的结合在复杂的现实临床环境中已证明有效。该方法为患者分层和药物安全监测提供了一个更强大、精确且临床适应性更强的框架。
