Nocturnal hypoglycemia is a common phenomenon among patients with diabetes and can lead to a broad range of adverse events and complications. Identifying factors associated with hypoglycemia can improve glucose control and patient care. We propose a repeated measures random forest (RMRF) algorithm that can handle nonlinear relationships and interactions and the correlated responses from patients evaluated over several nights. Simulation results show that our proposed algorithm captures the informative variable more often than naïvely assuming independence. RMRF also outperforms standard random forest and extremely randomized trees algorithms. We demonstrate scenarios where RMRF attains greater prediction accuracy than generalized linear models. We apply the RMRF algorithm to analyze a diabetes study with 2,525 nights from 127 patients with type 1 diabetes. We find nocturnal hypoglycemia is associated with HbA1c, bedtime BG, insulin on board, time system activated, exercise intensity, and daytime hypoglycemia. The RMRF can accurately classify nights at high risk of nocturnal hypoglycemia. This article is protected by copyright. All rights reserved.

中文翻译：

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重复测量随机森林 (RMRF)：识别与夜间低血糖相关的因素

夜间低血糖是糖尿病患者的常见现象，可导致广泛的不良事件和并发症。识别与低血糖相关的因素可以改善血糖控制和患者护理。我们提出了一种重复测量随机森林 (RMRF) 算法，该算法可以处理非线性关系和相互作用，以及经过数晚评估的患者的相关反应。仿真结果表明，我们提出的算法比天真地假设独立性更频繁地捕获信息变量。RMRF 也优于标准随机森林和极端随机树算法。我们展示了 RMRF 比广义线性模型获得更高预测精度的场景。我们应用 RMRF 算法来分析一项糖尿病研究，其中包括 2、来自 127 名 1 型糖尿病患者的 525 个夜晚。我们发现夜间低血糖与 HbA1c、睡前血糖、船上胰岛素、时间系统激活、运动强度和白天低血糖有关。RMRF 可以准确地对夜间低血糖风险高的夜晚进行分类。本文受版权保护。版权所有。