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.

中文翻译：

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使用多智能体强化学习进行公正的特征选择以预测不良血糖事件

我们开发了一个注意力模型，根据 35 分钟内过去血糖值的观察，提前 30 分钟预测未来的不良血糖事件。所提出的模型使用 Time2Vec (T2V) 有效地编码胰岛素给药和进餐时间以进行血糖预测。所提出的公正特征选择算法旨在根据代理贡献按比例分配奖励。代理贡献是通过逐步否定更新代理来计算的。因此，所提出的特征选择算法优化电子病历的特征以提高性能。为了进行评估，我们收集了顺天乡大学天安医院收治的 102 名 2 型糖尿病患者的连续血糖监测数据。使用我们提出的模型，我们在正常血糖、低血糖和高血糖方面分别获得了 89.0%、60.6% 和 89.8% 的 F1 分数。