{"603003":{"#nid":"603003","#data":{"type":"event","title":"PhD Defense by Anthony Bonifonte","body":[{"value":"\u003Cp\u003ETitle:\u0026nbsp;Data-Driven Healthcare Management: An Analytics Approach to Blood Pressure Control and Cardiovascular Disease Prevention\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EAdvisor: Dr.\u0026nbsp;Turgay Ayer\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003ECommittee members (ordered alphabetically):\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Benjamin Haaland\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Kamran Paynabar\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Julie Swann\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Emir Veledar (Emory University)\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Yao Xie\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDate and Time:\u0026nbsp;Monday March 12, 10:00am\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003ELocation:\u0026nbsp;Groseclose 403\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003ESummary:\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThis thesis develops analytics based tools using operations research and statistical methodologies to address problems in the domain of cardiovascular disease and blood pressure management.\u0026nbsp; Cardiovascular disease is the leading cause of death in the United States and worlwide, and a major component of healthcare costs.\u0026nbsp; Elevated blood pressure (hypertension) has been shown as a significant risk factor for cardiovascular disease.\u0026nbsp; We develop data-driven mathematical models to optimize treatment decisions, monitor blood pressure from mobile health technologies, and maximize population health.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIn chapter I, we show that history of a patient\u0026#39;s blood pressure is an important predictor of future cardiovascular risk.\u0026nbsp; Standard risk prediction models consider only a patient\u0026#39;s current blood pressure and ignore history.\u0026nbsp; Using Cox model survival analysis and the Framingham Heart Study data set, we\u0026nbsp;demonstrate improved predictive ability by incorporating antecedent blood pressure measurements.\u0026nbsp; The results of this chapter motivate the following chapters by emphasizing the importance of a time integrated measure of blood pressure as a risk for cardiovascular disease.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIn chapter II, we develop a population level optimal antihypertensive treatment policy.\u0026nbsp; We model blood pressure as a continuous time, continuous state stochastic process, specifically a geometric Brownian motion mixture model, which we demonstrate is a good statistical fit.\u0026nbsp; Using published parameters of cardiovascular disease risk as a function of blood pressure, we create a closed form analytical expression for the expectation and variance of hazard a patient experiences over the following T years.\u0026nbsp; Using meta-analyses of randomized control trials, we estimate the effects of different dosages of antihypertensive treatment and optimize over treatment decisions.\u0026nbsp; We create a threshold based population level optimal treatment policy for initiation and intensification of antihypertensive treatment, and show significant improvement over current guidelines in a large scale simulation model.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIn chapter III, we develop two changepoint detection-based algorithms for monitoring blood pressure from wearables and other mobile health technologies, which can gather many more\u0026nbsp; measurements than traditional clinical measurements.\u0026nbsp; The first algorithm uses knowledge of the disease progression to maintain a Bayesian belief of the true state.\u0026nbsp; This method is highly accurate, but may be difficult to implement in practice due to the parameter estimation and necessity of simulation to calibrate the algorithm.\u0026nbsp; We subsequently develop a Naive changepoint detection algorithm that is simple and generalizable to other continuous health characteristics such as cholesterol, glucose level, and pulse.\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Data-Driven Healthcare Management: An Analytics Approach to Blood Pressure Control and Cardiovascular Disease Prevention  "}],"uid":"27707","created_gmt":"2018-02-27 19:52:38","changed_gmt":"2018-02-27 19:52:38","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2018-03-12T11:00:00-04:00","event_time_end":"2018-03-12T13:00:00-04:00","event_time_end_last":"2018-03-12T13:00:00-04:00","gmt_time_start":"2018-03-12 15:00:00","gmt_time_end":"2018-03-12 17:00:00","gmt_time_end_last":"2018-03-12 17:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}