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作者：Liu, Xueqing; Deliu, Nina; Chakraborty, Tanujit; Bell, Lauren; Chakraborty, Bibhas
作者单位：National University of Singapore; Sapienza University Rome; University of London; King's College London
摘要：Mobile health (mHealth) interventions often aim to improve distal outcomes, such as clinical conditions, by optimizing proximal outcomes through just-in-time adaptive interventions. Contextual bandits provide a suitable framework for customizing such interventions according to individual time-varying contexts. However, unique challenges, such as modeling count outcomes within bandit frameworks, have hindered the widespread application of contextual bandits to mHealth studies. The current work ...
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作者：Mondal, Debashis; Chang, Xiaohui
作者单位：Washington University (WUSTL); Oregon State University
摘要：Environmental bioassays, such as sediment toxicity tests, provide abroad survey of toxicity that is crucial for the conservation and protection of marine and estuarine ecosystems. Using odds, risk, and survival probability ratios, this paper presents a critical evaluation of sediment toxicity tests data collected in the New York-New Jersey harbor area. It further derives spatial regression analysis to combine test results, predict toxicity at unsampled locations, and determine the effects of s...
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作者：Liu, Xin; Schnell, Patrick M.
作者单位：University System of Ohio; Ohio State University
摘要：Electronic medical records (EMR) data contain rich information that can facilitate health-related studies but is collected primarily for purposes other than research. For recurrent events, EMR data often do not record event times or counts but only contain intermittently assessed and censored observations (i.e., upper and/or lower bounds for counts in a time interval) at uncontrolled times. This can result in noncontiguous or overlapping assessment intervals with censored event counts. Existin...
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作者：Medom-Nnamdi, Patrick; Smith, Timothy R.; Onnela, Jukka-Pekka; Lu, Junwei
作者单位：Harvard University; Harvard University; Harvard University Medical Affiliates; Brigham & Women's Hospital; Harvard Medical School
摘要：We propose a nonparametric additive model for estimating interpretable value functions in reinforcement learning, with an application in optimizing postoperative recovery through personalized, adaptive recommendations. While reinforcement learning has achieved significant success in various domains, recent methods often rely on black-box approaches, such as neural networks, which hinder the examination of individual feature contributions to a decision-making policy. Our novel method offers a f...
