A Deep Reinforcement Learning Approach for Type 2 Diabetes Mellitus Treatment

Abstract

Type 2 diabetes mellitus (T2DM) is a chronic disease that requires continuous treatments. T2DM treatments aim to achieve not only short-term but, more importantly, long-term control of the patient's glucose to a normal level. We believe Reinforcement Learning (RL) can be an effective approach to learn and further recommend the ideal sequence of treatments that optimize the patient's long-term outcome. In this paper, we implement RL with the deep Q network. Our RL model learns from a T2DM patient registry dataset that consists of the clinical data of newly diagnosed T2DM patients over a twelve-month period including four follow-ups since their first visit. The RL model is trained to recommend the number of oral antidiabetic drugs and the number of insulins. Our experiments have shown that our RL model can improve the long-term hemoglobin A1c goal achieving rate by up to 15%. This confirms that the model learns good medication patterns that favor the long-term glucose control.