Impartial feature selection using multi-agent reinforcement learning for adverse glycemic event prediction

We developed an attention model to predict future adverse glycemic events 30 min in advance based on the observation of past glycemic values over a 35 min period. The proposed model effectively encodes insulin administration and meal intake time using Time2Vec (T2V) for glucose prediction. The proposed impartial feature selection algorithm is designed to distribute rewards proportionally according to agent contributions. Agent contributions are calculated by a step-by-step negation of updated agents. Thus, the proposed feature selection algorithm optimizes features from electronic medical records to improve performance. For evaluation, we collected continuous glucose monitoring data from 102 patients with type 2 diabetes admitted to Cheonan Hospital, Soonchunhyang University. Using our proposed model, we achieved F1-scores of 89.0%, 60.6%, and 89.8% for normoglycemia, hypoglycemia, and hyperglycemia, respectively. •We developed a glycemic event prediction model forT2DM patients.•We collected a extensive dataset including blood glucose, insulin doses, meal times, and EMR data.•Our model uses multi-agent RL to assess variable contributions for feature selection fairly.•Enhanced performance by encoding time sequence data like insulin intake with T2V into high-dimensional space.