We developed three machine learning models that predict hour-by-hour probabilities of a future lapse back to alcohol use with increasing temporal precision (i.e., lapses in the next week, next day, and next hour). Model features were based on raw scores and longitudinal change in theoretically implicated risk factors collected through ecological momentary assessment. Participants (N = 151, 51% male, Mage = 41, 87% White, 97% non-Hispanic) in early recovery (1-8 weeks of abstinence) from alcohol use disorder provided 4 × daily ecological momentary assessment for up to 3 months. We used grouped, nested cross-validation to select the best models and evaluate the performance of those best models. Models yielded median areas under the receiver operating curves of 0.89, 0.90, and 0.93 in the 30 held-out test sets for week-, day-, and hour-level models, respectively. Some feature categories consistently emerged as being globally important to lapse prediction across our week-, day-, and hour-level models (i.e., past use, future self-efficacy). However, most of the more punctate, time-varying constructs (e.g., craving, past stressful events, arousal) appear to have a greater impact within the next-hour prediction model. This research represents an important step toward the development of a smart (machine learning guided) sensing system that can both identify periods of peak lapse risk and recommend specific supports to address factors contributing to this risk. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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用于酒精使用障碍时间精确延迟预测的机器学习模型。


我们开发了三个机器学习模型，可以逐小时预测未来恢复饮酒的概率，时间精度越来越高（即下周、第二天和下一小时的失误）。模型特征基于原始分数和通过生态瞬时评估收集的理论上涉及的风险因素的纵向变化。从酒精使用障碍中早期恢复（戒酒 1-8 周）的参与者（N = 151,51% 男性，Mage = 41,87% 白人，97% 非西班牙裔）提供长达 3 个月的每日 4 ×生态瞬时评估。我们使用分组、嵌套的交叉验证来选择最佳模型并评估这些最佳模型的性能。在周、日和小时水平模型的 30 个保留测试集中，模型在受试者工作曲线下的中位数分别为 0.89、0.90 和 0.93。在我们的周、日和小时级别模型中，一些特征类别始终被视为对失误预测具有全局重要性（即，过去的使用、未来的自我效能感）。然而，大多数更点状、时变的结构 （例如，渴望、过去的应激事件、觉醒） 似乎在下一个小时的预测模型中具有更大的影响。这项研究代表了朝着开发智能（机器学习引导）传感系统迈出的重要一步，该系统既可以识别高峰延迟风险的时期，又可以推荐具体的支持来解决导致这种风险的因素。（PsycInfo 数据库记录 （c） 2024 APA，保留所有权利）。