Estimating Dynamic Treatment Regimes in Mobile Health Using V-learning.
Estimating Dynamic Treatment Regimes in Mobile Health Using V-learning.
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DOI：
10.1080/01621459.2018.1537919
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发表时间：
2020
影响因子：
3.7
通讯作者：
Kosorok MR
中科院分区：
文献类型：
--
作者：
Luckett DJ;Laber EB;Kahkoska AR;Maahs DM;Mayer-Davis E;Kosorok MR
精准医疗的愿景是利用个体患者的特征来制定个性化的治疗计划，为每位患者提供最佳的医疗保健。移动的技术在这一愿景中发挥着重要作用，因为它们提供了一种实时监测患者健康状况的手段，并随后在需要时提供干预措施。动态治疗方案将个性化治疗计划形式化为决策规则序列，每个临床干预阶段一个，将当前患者信息映射到推荐治疗。然而，大多数现有的方法，估计最佳的动态治疗方案是专为少数的固定决策点发生在一个粗略的时间尺度。我们提出了一种新的强化学习方法，用于估计最佳治疗方案，该方法适用于在门诊患者环境中使用移动的技术收集的数据。所提出的方法适应了一个不确定的时间范围和逐分钟的决策是常见的移动的健康应用程序。在较弱的条件下，我们证明了所提出的估计是相合的和渐近正态的。所提出的方法应用于估计一个最佳的动态治疗方案，用于控制1型糖尿病患者的血糖水平。
The vision for precision medicine is to use individual patient characteristics to inform a personalized treatment plan that leads to the best possible healthcare for each patient. Mobile technologies have an important role to play in this vision as they offer a means to monitor a patient’s health status in real-time and subsequently to deliver interventions if, when, and in the dose that they are needed. Dynamic treatment regimes formalize individualized treatment plans as sequences of decision rules, one per stage of clinical intervention, that map current patient information to a recommended treatment. However, most existing methods for estimating optimal dynamic treatment regimes are designed for a small number of fixed decision points occurring on a coarse time-scale. We propose a new reinforcement learning method for estimating an optimal treatment regime that is applicable to data collected using mobile technologies in an outpatient setting. The proposed method accommodates an indefinite time horizon and minute-by-minute decision making that are common in mobile health applications. We show that the proposed estimators are consistent and asymptotically normal under mild conditions. The proposed methods are applied to estimate an optimal dynamic treatment regime for controlling blood glucose levels in patients with type 1 diabetes.
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影响因子：
1.5
作者：
Long, Nanye;Gianola, Daniel;Gonzalez-Recio, Oscar
通讯作者：
Gonzalez-Recio, Oscar
影响因子：
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作者：
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通讯作者：
Swendeman, Dallas
DOI：
10.1080/01621459.2016.1155993
发表时间：
2017-06-01
影响因子：
3.7
作者：
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通讯作者：
Stefanski, Leonard A.
影响因子：
5.4
作者：
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通讯作者：
McMurray, Robert G.
影响因子：
1.9
作者：
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通讯作者：
Stephens, David A.