Adaptive learning algorithms to optimize mobile applications for behavioral health: guidelines for design decisions.
Adaptive learning algorithms to optimize mobile applications for behavioral health: guidelines for design decisions.
复制标题
用于优化移动应用程序以实现行为健康的自适应学习算法：设计决策指南。
DOI：
10.1093/jamia/ocab001
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Lyles,CourtneyR
中科院分区：
文献类型：
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作者：
Figueroa,CarolineA;Aguilera,Adrian;Chakraborty,Bibhas;Modiri,Arghavan;Aggarwal,Jai;Deliu,Nina;Sarkar,Urmimala;JayWilliams,Joseph;Lyles,CourtneyR
通过智能手机提供行为健康干预措施，使这些干预措施能够适应个人不断变化的行为，偏好和需求。这可以通过强化学习（RL）来实现，这是机器学习的一个子领域。然而，许多挑战可能会影响这些算法在真实的世界中的有效性。我们提供决策的指导方针。材料和MethodsUsing专题分析，我们描述的挑战，考虑因素和解决方案的算法设计决策之间的合作卫生服务研究人员，临床医生和数据科学家。我们使用RL算法的设计过程中的一个移动的健康研究“DIAMANTE”增加体力活动不足的糖尿病和抑郁症患者。在这个为期一年半的项目中，我们使用协作云Google Documents、Whatsapp Messenger和视频电话会议来跟踪研究过程。我们讨论、分类和编码了关键挑战。我们分组的挑战，以创建主题的主题过程domains.ResultsNine挑战出现，我们分为3大主题：1。选择决策模型，包括适当的背景和奖励变量; 2.数据处理/收集，例如如何实时处理丢失或不正确的数据; 3.权衡算法性能与有效性/实现在现实世界setting.ConclusionThe创建有效的行为健康干预措施并不只取决于最终的算法性能。真实的世界中的许多决策对于公式化应用算法的问题参数的设计是必要的。研究人员必须在干预之前和干预期间记录和评估这些考虑和决定，以增加透明度，问责制和可重复性。Registrationclinicaltrials.gov
ObjectiveProviding behavioral health interventions via smartphones allows these interventions to be adapted to the changing behavior, preferences, and needs of individuals. This can be achieved through reinforcement learning (RL), a sub-area of machine learning. However, many challenges could affect the effectiveness of these algorithms in the real world. We provide guidelines for decision-making.Materials and MethodsUsing thematic analysis, we describe challenges, considerations, and solutions for algorithm design decisions in a collaboration between health services researchers, clinicians, and data scientists. We use the design process of an RL algorithm for a mobile health study “DIAMANTE” for increasing physical activity in underserved patients with diabetes and depression. Over the 1.5-year project, we kept track of the research process using collaborative cloud Google Documents, Whatsapp messenger, and video teleconferencing. We discussed, categorized, and coded critical challenges. We grouped challenges to create thematic topic process domains.ResultsNine challenges emerged, which we divided into 3 major themes: 1. Choosing the model for decision-making, including appropriate contextual and reward variables; 2. Data handling/collection, such as how to deal with missing or incorrect data in real-time; 3. Weighing the algorithm performance vs effectiveness/implementation in real-world settings.ConclusionThe creation of effective behavioral health interventions does not depend only on final algorithm performance. Many decisions in the real world are necessary to formulate the design of problem parameters to which an algorithm is applied. Researchers must document and evaulate these considerations and decisions before and during the intervention period, to increase transparency, accountability, and reproducibility.Trial Registrationclinicaltrials.gov, NCT03490253.