The significance of medication therapy in managing comorbid diabetes is vital for maintaining the overall wellness of patients and reducing the cost of healthcare. Thus, using appropriate medication or medication combinations will be necessary for improved person-centred care and reduce complications associated with diagnosis and treatment. This study explains an intelligent decision support framework for managing 30 days unplanned readmission (30_URD) of comorbid diabetes using the Random Forest (RF) algorithm and Bayesian Network (BN) model. After the analysis of the medical records of 101,756 de-identified diabetic patients treated with 21 medications for 28 comorbidity combinations, the optimal medications for minimizing the likelihood of early readmissions were determined. This approach can help for identifying and managing most vulnerable patients thereby giving room to enhance post-discharge monitoring through clinical specialist supports to build critical-self management skills that will minimize the cost of diabetes care.

药物治疗在控制合并症中的重要性对于维持患者的整体健康并降低医疗保健成本至关重要。因此，使用适当的药物或药物组合对于改善以人为本的护理并减少与诊断和治疗相关的并发症将是必要的。这项研究介绍了一种智能决策支持框架，该框架使用随机森林（RF）算法和贝叶斯网络（BN）模型来管理30天的合并症糖尿病患者的计划外再次入院（30_URD）。在对101,756例未确认身份的糖尿病患者的病历进行分析之后，他们使用21种药物治疗了28种合并症，确定了将早期再次入院的可能性降至最低的最佳药物。