Zargoush Manaf, Ghazalbash Somayeh, Hosseini Mahsa Madani, Alemi Farrokh, Perri Dan
Health Policy and Management, DeGroote School of Business, McMaster University, Hamilton, ON, Canada.
Ted Rogers School of Management, Toronto Metropolitan University, Toronto, ON, Canada.
Sci Rep. 2025 Jul 23;15(1):26811. doi: 10.1038/s41598-025-12310-1.
The increasing prevalence of type 2 diabetes (T2D) is a significant health concern worldwide. Effective and personalized treatment strategies are essential for improving patient outcomes and reducing healthcare costs. Machine learning (ML) has the potential to create clinical decision support systems (CDSS) that assist clinicians in making prediction-informed treatment decisions. This study aims to develop a novel predictive-prescriptive analytics framework that leverages ML to enhance medication prescriptions for T2D patients. The framework is designed as a data-driven CDSS to determine the best medication strategies based on individual patient profiles, including demographics, comorbidities, and medications. Utilizing a comprehensive dataset of electronic health records from 17,773 patients across various U.S. Veterans Administration Medical Centers collected over 12 years, the study employs the Bayesian Network (BN) as the ML model of choice. The BN's unique dual capability serves both predictive and prescriptive functions. Several BN learning algorithms are applied to map the relationships among patient features and decision variables for predicting the outcome. The prescriptive stage includes three strategies, i.e., forward, backward, and guideline-based, to identify optimal treatment recommendations. Next, the complex treatment pathways identified through the prescriptive stage were illustrated using rule-based and decision-tree presentations to improve interpretability for actionable insights and clinical usability. Finally, our empirical analysis examines the alignment between recommended treatment strategies and actual physician prescriptions. ML exhibited strong predictive performance with a precision of 0.789, a recall of 0.879, and an F1-score of 0.831. The recommended treatment strategies aligned with physician prescriptions in simpler treatment scenarios. However, the alignment decreased as the complexity of medication prescription increased, highlighting the challenges of achieving physician compliance with optimal strategies in complex scenarios. This underscores the greater need for CDSS, particularly in situations involving complex combination therapy. This study presents a novel ML-based CDSS framework for personalized T2D treatment. Leveraging ML, the framework offers a promising approach to optimizing medication prescriptions and improving patient outcomes.
2型糖尿病（T2D）患病率的不断上升是全球重大的健康问题。有效且个性化的治疗策略对于改善患者预后和降低医疗成本至关重要。机器学习（ML）有潜力创建临床决策支持系统（CDSS），协助临床医生做出基于预测的治疗决策。本研究旨在开发一种新颖的预测 - 规范分析框架，利用ML增强T2D患者的药物处方。该框架被设计为一个数据驱动的CDSS，以根据个体患者特征（包括人口统计学、合并症和用药情况）确定最佳用药策略。该研究利用来自美国各地退伍军人管理局医疗中心的17773名患者在12年期间收集的综合电子健康记录数据集，采用贝叶斯网络（BN）作为首选的ML模型。BN独特的双重功能兼具预测和规范作用。应用几种BN学习算法来映射患者特征与决策变量之间的关系以预测结果。规范阶段包括三种策略，即向前、向后和基于指南的策略，以确定最佳治疗建议。接下来，使用基于规则和决策树的展示方式来说明通过规范阶段确定的复杂治疗路径，以提高可操作性见解的可解释性和临床实用性。最后，我们的实证分析检验了推荐的治疗策略与实际医生处方之间的一致性。ML表现出强大的预测性能，精确率为0.789，召回率为0.879，F1分数为0.831。在较简单的治疗场景中，推荐的治疗策略与医生处方一致。然而，随着药物处方复杂性的增加，一致性下降，凸显了在复杂场景中使医生遵循最佳策略的挑战。这强调了对CDSS的更大需求，特别是在涉及复杂联合治疗的情况下。本研究提出了一种用于个性化T2D治疗的基于ML的新型CDSS框架。该框架利用ML，为优化药物处方和改善患者预后提供了一种有前景的方法。
