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.
Keywords: 30 days readmission; Bayesian network; Comorbidity; Diabetes; Medication therapy; Random forest.
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