2017
DOI: 10.1136/bmjopen-2017-018374
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Dynamic treatment selection and modification for personalised blood pressure therapy using a Markov decision process model: a cost-effectiveness analysis

Abstract: ObjectivePersonalised medicine seeks to select and modify treatments based on individual patient characteristics and preferences. We sought to develop an automated strategy to select and modify blood pressure treatments, incorporating the likelihood that patients with different characteristics would benefit from different types of medications and dosages and the potential severity and impact of different side effects among patients with different characteristics.Design, setting and participantsWe developed a M… Show more

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Cited by 9 publications
(17 citation statements)
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“…Markov Decision Process (MDP) is a model where the results are partially under control of a decision maker based on high-quality probabilistic calculations with meta-analytic data [12]. Random Forest Algorithm (RFA) searches for relevant important characteristics during classification, being a technique that seeks to achieve a maximum classification accuracy rate [13].…”
Section: Results E Discussionmentioning
confidence: 99%
“…Markov Decision Process (MDP) is a model where the results are partially under control of a decision maker based on high-quality probabilistic calculations with meta-analytic data [12]. Random Forest Algorithm (RFA) searches for relevant important characteristics during classification, being a technique that seeks to achieve a maximum classification accuracy rate [13].…”
Section: Results E Discussionmentioning
confidence: 99%
“…33 In addition, a series of parameters have to be set artificially in MCL and thus analyst blinding can hardly be achieved. 43 Current findings need to be further validated and expanded by other data mining approaches. 34 Strengths of this study design should also be noted.…”
Section: Discussionmentioning
confidence: 97%
“…Therefore, it has an ability to describe the internal property and external relevance with high efficiency in a dimension-reducing process (typically TCM syndrome patterns validation). 42,43 ''Memorylessness'' in MCL refers to an assumption that the properties of variables related to the future depend only on their current state, not upon the sequence of events that preceded it. 44 The major limitation of the MCL model is ''memorylessness,'' since diagnosis and prognosis for each patient usually rest on the history of previous diseases due to the Markov property of nonafter effect, which may affect the sensitivity and validity of results.…”
Section: Discussionmentioning
confidence: 99%
“…Recent literature discussed various efforts on constructing and expediting models with dramatically complex structures. However, there are scarce discussions about fundamental parameters such as cycle lengths, half-a-cycle age correction and background probabilities (7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18). Moreover, these parameters have been used without justification in many recent implementations and applications of Markov microsimulation models (4)(5)(6).…”
Section: Introductionmentioning
confidence: 99%