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We introduce a framework for developing adaptive, personalized treatments for such chronic diseases. Our model is based on a continuous-time, multi-armed bandit setting where drug effectiveness is assessed by aggregating information from several channels: by continuously monitoring the state of the patient, but also by (not) observing the occurrence of particular infrequent health events, such as relapses or disease flare-ups. Recognizing that the timing and severity of such events provide critical information for treatment decisions is a key point of departure in our framework compared with typical (bandit) models used in healthcare. We show that the model can be analyzed in closed form for several settings of interest, resulting in optimal policies that are intuitive and may have practical appeal. We illustrate the effectiveness of the methodology by developing a set of efficient treatment policies for multiple sclerosis, which we then use to benchmark several existing treatment guidelines.<\/jats:p>The online appendix is available at https:\/\/doi.org\/10.1287\/mnsc.2017.2793 .<\/jats:p>This paper was accepted by Noah Gans, stochastic models and simulation.<\/jats:p>","DOI":"10.1287\/mnsc.2017.2793","type":"journal-article","created":{"date-parts":[[2017,8,21]],"date-time":"2017-08-21T19:06:13Z","timestamp":1503342373000},"page":"3469-3488","source":"Crossref","is-referenced-by-count":42,"title":["Dynamic Learning of Patient Response Types: An Application to Treating Chronic Diseases"],"prefix":"10.1287","volume":"64","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-8294-275X","authenticated-orcid":false,"given":"Diana M.","family":"Negoescu","sequence":"first","affiliation":[{"name":"Industrial and Systems Engineering Department, University of Minnesota, Minneapolis, 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