Effective data-driven precision medicine by cluster-applied deep reinforcement learning

The significance of machine-learning approaches in the healthcare domain has grown rapidly owing to the existence of enormous amounts of data and well-established simulation models and algorithms. The digitization of health-related data, as well as rapid technological advancements are accelerating the development and application of machine learning in healthcare, particularly in precision medicine. The ultimate goal of precision medicine is to provide personalized medicine, which requires tailoring medical decisions to each patient based on their projected disease response. In this study, we propose a cluster-applied deep reinforcement learning-based type 2 diabetes treatment recommendation model based on the electronic health records of South Koreans. The purpose of applying a clustering algorithm is to group patients who are in a similar state, to boost the performance of deep reinforcement learning, build a more realistic treatment recommendation model to support clinicians, and develop expert systems in the field of healthcare. The proposed model demonstrated significant performance by decreasing diabetes-related medical checkup measurements. Furthermore, the proposed model delivered high-quality performance when compared with existing reinforcement-learning methods. Finally, the recommendation outcomes of the proposed model were validated against real-life prescriptions to ensure the accuracy of the findings.