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Data Analytics And Stochastic Optimization Models For Decision Support In Chronic Disease Operations Management
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Book Synopsis Data Analytics and Stochastic Optimization Models for Decision Support in Chronic Disease Operations Management by : Mohammad Hessam Olya
Download or read book Data Analytics and Stochastic Optimization Models for Decision Support in Chronic Disease Operations Management written by Mohammad Hessam Olya and published by . This book was released on 2019 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: The results of this study show that feature representation and training related instances jointly increase the performance of patient workload prediction. Moreover, we have addressed two critical issues in team-based healthcare strategic and tactical planning. The first issue is to determine the optimal number of providers for multiple facilities and eligible patients for pay-to-travel incentives where the demand and location of patients are unknown. The second issue is to minimize the number of different healthcare teams and balance their workload within every single facility. We have developed a stochastic workforce and workload optimization model under various scenarios to address this issue. The result of prescriptive analysis suggests considering the randomness rather than replacing the stochastic variables by their expected value significantly contributes in reducing the overall cost of healthcare and practically enhancing access to care.
Book Synopsis Decision Analytics and Optimization in Disease Prevention and Treatment by : Nan Kong
Download or read book Decision Analytics and Optimization in Disease Prevention and Treatment written by Nan Kong and published by John Wiley & Sons. This book was released on 2018-03-13 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt: A systematic review of the most current decision models and techniques for disease prevention and treatment Decision Analytics and Optimization in Disease Prevention and Treatment offers a comprehensive resource of the most current decision models and techniques for disease prevention and treatment. With contributions from leading experts in the field, this important resource presents information on the optimization of chronic disease prevention, infectious disease control and prevention, and disease treatment and treatment technology. Designed to be accessible, in each chapter the text presents one decision problem with the related methodology to showcase the vast applicability of operations research tools and techniques in advancing medical decision making. This vital resource features the most recent and effective approaches to the quickly growing field of healthcare decision analytics, which involves cost-effectiveness analysis, stochastic modeling, and computer simulation. Throughout the book, the contributors discuss clinical applications of modeling and optimization techniques to assist medical decision making within complex environments. Accessible and authoritative, Decision Analytics and Optimization in Disease Prevention and Treatment: Presents summaries of the state-of-the-art research that has successfully utilized both decision analytics and optimization tools within healthcare operations research Highlights the optimization of chronic disease prevention, infectious disease control and prevention, and disease treatment and treatment technology Includes contributions by well-known experts from operations researchers to clinical researchers, and from data scientists to public health administrators Offers clarification on common misunderstandings and misnomers while shedding light on new approaches in this growing area Designed for use by academics, practitioners, and researchers, Decision Analytics and Optimization in Disease Prevention and Treatment offers a comprehensive resource for accessing the power of decision analytics and optimization tools within healthcare operations research.
Book Synopsis Data, Models and Decisions for Large-scale Stochastic Optimization Problems by : Velibor V. Mišić
Download or read book Data, Models and Decisions for Large-scale Stochastic Optimization Problems written by Velibor V. Mišić and published by . This book was released on 2016 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern business decisions exceed human decision making ability: often, they are of a large scale, their outcomes are uncertain, and they are made in multiple stages. At the same time, firms have increasing access to data and models. Faced with such complex decisions and increasing access to data and models, how do we transform data and models into effective decisions? In this thesis, we address this question in the context of four important problems: the dynamic control of large-scale stochastic systems, the design of product lines under uncertainty, the selection of an assortment from historical transaction data and the design of a personalized assortment policy from data. In the first chapter, we propose a new solution method for a general class of Markov decision processes (MDPs) called decomposable MDPs. We propose a novel linear optimization formulation that exploits the decomposable nature of the problem data to obtain a heuristic for the true problem. We show that the formulation is theoretically stronger than alternative proposals and provide numerical evidence for its strength in multi-armed bandit problems. In the second chapter, we consider to how to make strategic product line decisions under uncertainty in the underlying choice model. We propose a method based on robust optimization for addressing both parameter uncertainty and structural uncertainty. We show using a real conjoint data set the benefits of our approach over the traditional approach that assumes both the model structure and the model parameters are known precisely. In the third chapter, we propose a new two-step method for transforming limited customer transaction data into effective assortment decisions. The approach involves estimating a ranking-based choice model by solving a large-scale linear optimization problem, and solving a mixed-integer optimization problem to obtain a decision. Using synthetic data, we show that the approach is scalable, leads to accurate predictions and effective decisions that outperform alternative parametric and non-parametric approaches. In the last chapter, we consider how to leverage auxiliary customer data to make personalized assortment decisions. We develop a simple method based on recursive partitioning that segments customers using their attributes and show that it improves on a "uniform" approach that ignores auxiliary customer information.
Book Synopsis Data Analytics and Stochastic Models for Informed Decision Making in Healthcare by : Coralys M. Colón Morales
Download or read book Data Analytics and Stochastic Models for Informed Decision Making in Healthcare written by Coralys M. Colón Morales and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantitative methods make use of complex mathematical or statistical models to identify patterns in data, predict behaviors and support decision-making. These methods have been broadly applied in many fields. However, the healthcare industry is still ripe with opportunity. Cutting-edge quantitative analysis has only recently emerged in within healthcare. The focus of this dissertation is to continue bridging the gap between quantitative methods and the healthcare industry. Specifically, the work focuses on individual decision-making in the form of selecting a health insurance plan, and operational decision-making in the form of patient appointment scheduling. The uncertainty surrounding these decisions make them complex ones. By applying data analytics and stochastic modeling, the research presented here addresses the processes of decision-making under uncertainty within these settings.
Book Synopsis Operations Research and Health Care by : Margaret L. Brandeau
Download or read book Operations Research and Health Care written by Margaret L. Brandeau and published by Springer Science & Business Media. This book was released on 2006-04-04 with total page 870 pages. Available in PDF, EPUB and Kindle. Book excerpt: In both rich and poor nations, public resources for health care are inadequate to meet demand. Policy makers and health care providers must determine how to provide the most effective health care to citizens using the limited resources that are available. This chapter describes current and future challenges in the delivery of health care, and outlines the role that operations research (OR) models can play in helping to solve those problems. The chapter concludes with an overview of this book – its intended audience, the areas covered, and a description of the subsequent chapters. KEY WORDS Health care delivery, Health care planning HEALTH CARE DELIVERY: PROBLEMS AND CHALLENGES 3 1.1 WORLDWIDE HEALTH: THE PAST 50 YEARS Human health has improved significantly in the last 50 years. In 1950, global life expectancy was 46 years [1]. That figure rose to 61 years by 1980 and to 67 years by 1998 [2]. Much of these gains occurred in low- and middle-income countries, and were due in large part to improved nutrition and sanitation, medical innovations, and improvements in public health infrastructure.
Book Synopsis Sensing, Modeling and Optimization of Cardiac Systems by : Hui Yang
Download or read book Sensing, Modeling and Optimization of Cardiac Systems written by Hui Yang and published by Springer Nature. This book was released on 2023-09-19 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews the development of physics-based modeling and sensor-based data fusion for optimizing medical decision making in connection with spatiotemporal cardiovascular disease processes. To improve cardiac care services and patients’ quality of life, it is very important to detect heart diseases early and optimize medical decision making. This book introduces recent research advances in machine learning, physics-based modeling, and simulation optimization to fully exploit medical data and promote the data-driven and simulation-guided diagnosis and treatment of heart disease. Specifically, it focuses on three major topics: computer modeling of cardiovascular systems, physiological signal processing for disease diagnostics and prognostics, and simulation optimization in medical decision making. It provides a comprehensive overview of recent advances in personalized cardiac modeling by integrating physics-based knowledge of the cardiovascular system with machine learning and multi-source medical data. It also discusses the state-of-the-art in electrocardiogram (ECG) signal processing for the identification of disease-altered cardiac dynamics. Lastly, it introduces readers to the early steps of optimal decision making based on the integration of sensor-based learning and simulation optimization in the context of cardiac surgeries. This book will be of interest to researchers and scholars in the fields of biomedical engineering, systems engineering and operations research, as well as professionals working in the medical sciences.
Book Synopsis TIMS/ORSA Bulletin by : Institute of Management Sciences
Download or read book TIMS/ORSA Bulletin written by Institute of Management Sciences and published by . This book was released on 1983 with total page 566 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Predictive and Prescriptive Methods in Operations Research and Machine Learning by : Nishanth Mundru
Download or read book Predictive and Prescriptive Methods in Operations Research and Machine Learning written by Nishanth Mundru and published by . This book was released on 2019 with total page 221 pages. Available in PDF, EPUB and Kindle. Book excerpt: The availability and prevalence of data have provided a substantial opportunity for decision makers to improve decisions and outcomes by effectively using this data. In this thesis, we propose approaches that start from data leading to high-quality decisions and predictions in various application areas. In the first chapter, we consider problems with observational data, and propose variants of machine learning (ML) algorithms that are trained by taking into account decision quality. The traditional approach to such a task has often focused on two-steps, separating the estimation task from the subsequent optimization task which uses these estimated models. Consequently, this approach can miss out on potential improvements in decision quality by considering these tasks jointly. Crucially, this leads to stronger prescriptive performance, particularly for smaller training set sizes, and improves the decision quality by 3 − 5% over other state-of-the-art methods. We introduce the idea of uncertainty penalization to control the optimism of these methods which improves their performance, and propose finite-sample regret bounds. Through experiments on real and synthetic data sets, we demonstrate the value of this approach. In the second chapter, we consider observational data with decision-dependent uncertainty; in particular, we focus on problems with a finite number of possible decisions (treatments). We present our method of prescriptive trees, that prescribes the best treatment option by learning from observational data while simultaneously predicting counterfactuals. We demonstrate the effectiveness of such an approach using real data for the problem of personalized diabetes management. In the third chapter, we consider stochastic optimization problems when the sample average approximation approach is computationally expensive. We introduce a novel measure, called the Prescriptive divergence which takes into account the decision quality of the scenarios, and consider scenario reduction in this context. We demonstrate the power of this optimization-based approach on various examples. In the fourth chapter, we present our work on a problem in predictive analytics where we focus on ML problems from a modern optimization perspective. For sparse shape-constrained regression problems, we propose modern optimization based algorithms that are scalable, and recover the true support with high accuracy and low false positive rates.
Book Synopsis Data-Driven Decision-Making Under Uncertainty with Applications in Healthcare and Energy Management by : Saeed Ghodsi
Download or read book Data-Driven Decision-Making Under Uncertainty with Applications in Healthcare and Energy Management written by Saeed Ghodsi and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Decision-making under uncertainty has been studied for a long time by the operations management research community. In the past, uncertainty models were often derived based on domain knowledge. However, the availability of vast amounts of data in the recent years has shifted interests towards data-driven approaches for uncertainty quantification. More specifically, statistical models are employed within this framework for characterizing the uncertain components of a stochastic optimization problem based on historical data. In this dissertation, we focus on applications of data-driven decision-making under uncertainty in the healthcare and energy management sectors. The first part of our work provides a mathematical framework for efficient call assignment under Direct Load Control (DLC) contracts (i.e. an incentive-based demand-response program that is widely used by utility firms for balancing the supply and demand of electricity during peak times). Specifically, we employ a model for forecasting energy consumption and develop a large-scale integer stochastic dynamic optimization problem. We then propose a novel hierarchical approximation scheme for efficient execution of the contracts. We evaluate the quality of our proposed approach using real-world data obtained from California Independent System Operator (CAISO), which is the umbrella organization of utility firms in California. A large utility firm in California has implemented our model and informed us that they have experienced a 4\% additional r duction in their cost. Following a similar predict-then-optimize methodological framework, the second part of this dissertation studies data-driven healthcare intervention planning. Specifically, we develop a continuous-time latent-space Markovian model for describing disease progression based on discrete-time irregularly-spaced observations. Our model is capable of incorporating the effect of interventions on progression of disease. We discuss the computational challenges of parameter estimation for this model and present a novel efficient estimation approach based on the Expectation-Maximization (EM) algorithm. A population-level optimization model for intervention planning in the behavioral healthcare sector is then developed using the fitted disease progression model. Afterward, we present an extension of the model, which is more appropriate for medical healthcare domains such as cancer maintenance therapy, and formulate an EM algorithm for estimating the model parameters. Finally, we develop an individual-level intervention planning problem based on the patient's historical data using the estimated model.
Book Synopsis Operational Research for Emergency Planning in Healthcare: Volume 2 by : Navonil Mustafee
Download or read book Operational Research for Emergency Planning in Healthcare: Volume 2 written by Navonil Mustafee and published by Springer. This book was released on 2016-01-26 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a selection of studies that have applied Operational Research methods to improve emergency planning in healthcare, to include both A&E and public health emergencies like epidemic and natural disasters. The studies have delved into qualitative Operational Research like Problem Structuring, Critical Systems Thinking, Soft Systems Methodology, and Qualitative System Dynamics, and also quantitative techniques such as Monte Carlo Simulation, Discrete-event Simulation, and System Dynamics. These techniques have been applied for review and assessment of emergency services, for policy formulation and for facilitating broader public engagement in emergency preparedness and response. Furthermore, this book presents rigorous reviews on the applications of Operational Research in the wider healthcare context. This volume focuses mainly on emergency planning at the strategic level, whereas volume 1 focuses on planning at the operational level. The OR Essentials series presents a unique cross-section of high quality research work fundamental to understanding contemporary issues and research across a range of Operational Research (OR) topics. It brings together some of the best research papers from the highly respected journals of the Operational Research Society, also published by Palgrave Macmillan.
Book Synopsis Decision-analytic Models for Treatment Optimization in the Presence of Patient Heterogeneity by : Mutita Siriruchatanon
Download or read book Decision-analytic Models for Treatment Optimization in the Presence of Patient Heterogeneity written by Mutita Siriruchatanon and published by . This book was released on 2021 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the ever-increasing complexity in disease etiology, new therapeutics, healthcare service delivery, and clinical guidelines, selecting the appropriate course of treatment for an individual or population can become a great challenge for clinicians and healthcare providers. Applying suboptimal healthcare policies can set a damaging course for infectious disease control on the population level. On the individual level, disease can progress uniquely from patient-to-patient and ignoring a patient's preference may lead to treatment nonadherence or treatment rejection. In this thesis, we address the need for the development of decision-analytic methods for treatment selection that accounts for diversity in the patient population, uncertainty in patient treatment responses, and patients' preferences by studying the following problems: 1) the HIV treatment policy selection in HIV-infected children in sub-Saharan Africa initiating treatment at age >̲ 3 years old in the presence of pre-treatment drug resistance; 2) a personalized treatment selection problem for chronic depression where patient's respond uniquely to treatments whose response level is quantified by their unknown treatment effects; 3) a personalized treatment selection problem with two competing objectives, health outcomes and treatment side effect burden, given the qualitative rankings of sequences of possible patient's treatment and responses For the first problem, we develop and calibrate a microsimulation model of HIV disease progression and treatment. Using the model, we evaluate alternative antiretroviral treatment strategies using cost-effectiveness analyses. The second problem is formulated as a Markov Decision Process (MDP) where the treatment progression is parametrized by an individual's unknown treatment effects. We solved for the personalized treatment policies using two heuristic approaches: a model-based approach that can estimate an individual's treatment effect and a model-free approach using reinforcement learning. Taking into account an individual's preferences over two objectives, we formulate the last problem as an MDP as well. We developed two search algorithms, exhaustive and heuristic search, to estimate a patient's preference and provide an optimal treatment plan. This thesis contributes in developing three decision-analytic models to support decisions in testing, monitoring, and treatment selection for two significant healthcare problems, specifically, HIV and chronic depression, and treatment selection incorporating patient's preference. In addition, our work provides a step towards the design of personalized treatment strategy for patients with chronic diseases in various scenarios.
Book Synopsis Applications of Stochastic and Optimization Models to Healthcare Research by : Joel Goh
Download or read book Applications of Stochastic and Optimization Models to Healthcare Research written by Joel Goh and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation studies how mathematical modeling can be used in conjunction with empirical data to provide insight into health policy and medical decision-making. We consider three specific questions. First, how should drug safety regulators implement a postmarketing drug surveillance system that accounts for multiple adverse events? Second, what is the aggregate contribution of workplace stressors toward poor health outcomes and health spending in the U.S.? Third, how should rigorous cost-effectiveness analyses be conducted for medical innovations, when data are scarce and unreliable? These are important questions that have thus far eluded definitive answers because existing data sources and models cannot be directly applied to answer these questions satisfactorily. Therefore, we try to address these questions by developing new data-driven mathematical models, which draw ideas from stochastic analysis and optimization theory. In Chapter 1, we develop a new method for postmarketing surveillance of a drug, in order to detect any adverse side effects that were not uncovered during pre-approval clinical trials. Because of the recent proliferation of electronic medical records, regulators can now observe person-level data on drug usage and adverse event incidence in a population. Potentially, they can use these data to monitor the drug, and flag it as unsafe if excessive adverse side effects are observed. There are two key features of this problem that make it challenging. First, the data are accumulated in time, which complicates the regulators' decision process. Second, adverse events that occur in the past can affect the risk that other adverse events occur in the future. We propose a drug surveillance method, called QNMEDS, which simultaneously addresses these two issues. QNMEDS is based on the paradigm of sequential hypothesis testing, and it works by continuously monitoring a vector-valued test-statistic process until it crosses a stopping boundary. Our analysis focuses on prescribing how this boundary should be designed. We use a queueing network to model the occurrence of events in patients, which also allows us to capture the correlations between adverse events. Exact analysis of the model is intractable, and we develop an asymptotic diffusion approximation to characterize the approximate distribution of the test-statistic process. We then use mathematical optimization to design the stopping boundary to control the false alarm rate below an exogenously-specified value and minimize the expected detection time. We conduct simulations to demonstrate that QNMEDS works as designed and has advantages over a heuristic that is based on the classical Sequential Probability Ratio Test. In Chapter 2, we describe a model-based approach to quantify the relationship between workplace stressors and health outcomes and cost. We considered ten stressors: Unemployment, lack of health insurance, exposure to shift work, long working hours, job insecurity, work-family conflict, low job control, high job demands, low social support at work, and low organizational justice. There is widespread empirical evidence that individual stressors are associated with poor health outcomes, but the aggregate health effect of the combination of these stressors is not well understood. Our goal was to estimate the overall contribution of these stressors toward (a) annual healthcare spending, and (b) annual mortality in the U.S. The central difficulty in deriving these estimates is the absence of a single, longitudinal dataset that records workers' exposure to various workplace stressors as well as their health outcomes and spending. Therefore, we developed a model-based approach to tackle this problem. The model has four input parameters which were estimated from separate data sources: (a) the joint distribution of workplace exposures in the U.S., which we estimated from the General Social Survey (GSS); (b) the relative risk of each outcome associated with each exposure, which we estimated from an extensive meta-analysis of the epidemiological literature; (c) the status-quo prevalence of each health outcome; and (d) the incremental cost of each health outcome, which were both estimated using the Medical Panel Expenditure Survey (MEPS). The model separately derives optimistic and conservative estimates of the effect of multiple workplace exposures on health, and uses an optimization-based approach to calculate upper and lower bounds around each estimate to account for the correlation between exposures. We find that more than 120,000 deaths per year and approximately 5-8% of annual healthcare costs are associated with and may be attributable to how U.S. companies manage their work force. Our results suggest that more attention should be paid to management practices as important contributors to health outcomes and costs in the U.S. In Chapter 3, we study the problem of assessing the cost-effectiveness of a medical innovation when data are scarce or highly uncertain. Models based on Markov chains are typically used for medical cost-effectiveness analyses. However, if such models are used for innovations, many elements of the chain's transition matrix may be very imprecise due to data scarcity. While sensitivity analyses can be used to assess the effect of a small number of uncertain parameters, they quickly become computationally intractable as the number of uncertainties grows. At present, only ad-hoc methods exist for performing such analyses when there are a large number of uncertain parameters. Our analysis focuses on an abstraction of this problem, which is how to calculate the best and worst discounted value of a Markov chain over an infinite horizon with respect to a vector of state-wise rewards, when many of its transition elements are only known up to an uncertainty set. We prove the following sharp result: If the uncertainty set has a row-wise property, which is a reasonable assumption for most applied problems, then these values can be tractably computed by iteratively solving certain convex optimization problems. However, in the absence of this row-wise property, evaluating these values is computationally intractable (NP-hard). We apply our method to the evaluate the cost-effectiveness of a new screening method for colorectal cancer, annual fecal immunochemical testing (FIT) for persons over the age of 55. Our results suggest that FIT is a highly cost-effective alternative to the current guidelines, which prescribe screening by colonoscopy at 10-year intervals.
Book Synopsis Optimizing Personalized Treatment Selection for Partially Observable Chronic Conditions by : Jue Gong
Download or read book Optimizing Personalized Treatment Selection for Partially Observable Chronic Conditions written by Jue Gong and published by . This book was released on 2019 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: For many chronic diseases, an individual patient may experience a wide variety of progression pathways. Personalized medicine needs tools to predict the trajectory of an individual patient's disease progression, which can in turn enable clinicians to optimize the sequence of treatments. The objective of this thesis is to design artificial intelligence methods to support clinicians in making smart treatment selections in chronic disease care. To achieve this objective, we develop algorithms optimized for an individual patients' demographic profiles, past medical history, and response to current treatment by utilizing electronic health record (EHR) data of a large population. Developing a personalized treatment plan is a difficult sequential decision-making problem that seeks to improve overall health outcome, efficiency, and reduce unnecessary cost. One challenge is to understand the complex disease progression in a heterogeneous population. Another challenge is the lack of adaptive treatment strategies for diseases with partially observable health states. We develop innovative methodologies for personalized treatment selection to mitigate these challenges. First, we model the heterogeneity in disease trajectories of a population by detecting the subtypes of a chronic disease from longitudinal treatment data using an artificial neural network. Then we propose a framework called the partially observable collaborative model (POCM), to learn the individual disease progression model under various treatment options when the true health state is hidden to the decision maker. Next, utilizing the learned individual models, a personalized treatment plan can be derived by solving a partially observable Markov decision process (POMDP). We further extend this framework to mitigate the risk of reduced performance of POMDPs with uncertainty in transition dynamics by finding robust policies. Mental health is an understudied disease area that may greatly benefit from optimization in personalized medicine. Using simulated data informed by the Mental Health Research Network's EHR, we apply the proposed methods to simulate the treatment of chronic depression. The contributions of this thesis include a novel framework for learning personalized disease progression model, a robust and adaptive treatment selection method, and an application on chronic depression treatment optimization. This thesis helps to advance the development of artificial intelligent decision support tools for chronic disease care.
Book Synopsis Managing Uncertainty in Sequential Medical Decision Making by : Diana Maria Negoescu
Download or read book Managing Uncertainty in Sequential Medical Decision Making written by Diana Maria Negoescu and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Many models currently used to design and analyze health policies ignore uncertainty in patient outcomes, assume homogeneous patient response to interventions, and do not allow for sequential decision making. However, patient response to treatment is often highly variable; patient outcomes depend on various patient characteristics that can evolve stochastically over time; and decision makers need to respond to new states of the patient as they occur. In this dissertation, we apply stochastic optimization methods to design treatment policies that are adaptive to key patient characteristics in two health settings: treating HIV patients while considering potential long-term cardiovascular side effects and treating multiple sclerosis patients while adapting to their response to treatment. Antiretroviral therapy (ART) for HIV may increase the risk of cardiovascular morbidity and mortality, but delaying ART may diminish immunological benefits. The timing of ART initiation that balances these risks and benefits and yields maximum quality-adjusted life expectancy (QALE) is currently unknown. In Chapter 2, we develop a mathematical model to identify the timing of ART initiation that optimizes patient outcomes as a function of patient CD4 count, age, cardiac mortality risk, gender and personal preferences. Our goal is to find the conditions that maximize patient QALE. We find that, under the assumption that ART confers disease progression and mortality benefits at any CD4 count, immediate treatment initiation yields the greatest remaining QALE for young patients under most circumstances. However, delaying treatment initiation is preferable for older patients with high CD4 counts. The exact timing of ART initiation depends on the magnitude of benefit from ART at high CD4 counts, the magnitude of increases in cardiac risk, and patients' preferences. If ART reduces HIV progression at high CD4 counts, immediate ART is preferable for most newly infected individuals who are less than 35 years old even if ART doubles age- and gender-specific cardiac risk. In Chapter 3, we consider a class of chronic diseases where available treatments are effective only for a subgroup of patients, and biomarkers that accurately assess the responsiveness of an individual patient do not exist. In these settings, information regarding the response type of a patient can only be generated by experimentation - subjecting the patient to a variety of treatments. Physicians then learn about patient response through self-reported patient evaluations, as well as from the occurrence or nonoccurrence of negative health events such as disease flare-ups. The timing of these events also provides substantial information, which should be taken into account when determining optimal personalized treatments. We introduce a continuous-time, two-armed bandit framework that balances the trade-off between exploring alternative treatments and exploiting available information. Unlike most multi-armed bandit models that learn only from observed rewards, our model also incorporates information regarding the frequency of health events, and can be analyzed in closed form to derive guidelines for treatment policies. We illustrate the effectiveness of our methodology by developing an adaptive policy to treat multiple sclerosis, a chronic autoimmune disease. We compare the performance of our policy to that of a standard, non-adaptive treatment policy and show that, by identifying non-responders earlier, our approach leads to improvements in QALE, as well as significant cost savings. Beyond multiple sclerosis, dynamic learning models that incorporate the timing of events may have applications in a broader medical decision making context: for example, as a means to design treatment policies for chronic diseases such as depression, rheumatoid arthritis, celiac disease or Crohn's disease. We conclude with a discussion of the work and directions for future research in Chapter 4.
Book Synopsis Portfolio Decision Analysis by : Ahti Salo
Download or read book Portfolio Decision Analysis written by Ahti Salo and published by Springer Science & Business Media. This book was released on 2011-08-12 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: Portfolio Decision Analysis: Improved Methods for Resource Allocation provides an extensive, up-to-date coverage of decision analytic methods which help firms and public organizations allocate resources to 'lumpy' investment opportunities while explicitly recognizing relevant financial and non-financial evaluation criteria and the presence of alternative investment opportunities. In particular, it discusses the evolution of these methods, presents new methodological advances and illustrates their use across several application domains. The book offers a many-faceted treatment of portfolio decision analysis (PDA). Among other things, it (i) synthesizes the state-of-play in PDA, (ii) describes novel methodologies, (iii) fosters the deployment of these methodologies, and (iv) contributes to the strengthening of research on PDA. Portfolio problems are widely regarded as the single most important application context of decision analysis, and, with its extensive and unique coverage of these problems, this book is a much-needed addition to the literature. The book also presents innovative treatments of new methodological approaches and their uses in applications. The intended audience consists of practitioners and researchers who wish to gain a good understanding of portfolio decision analysis and insights into how PDA methods can be leveraged in different application contexts. The book can also be employed in courses at the post-graduate level.
Book Synopsis Biomedical Index to PHS-supported Research by :
Download or read book Biomedical Index to PHS-supported Research written by and published by . This book was released on 1990 with total page 1060 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author :Institute for Operations Research and the Management Sciences. National Meeting Publisher : ISBN 13 : Total Pages :644 pages Book Rating :4.E/5 ( download)
Book Synopsis INFORMS Annual Meeting by : Institute for Operations Research and the Management Sciences. National Meeting
Download or read book INFORMS Annual Meeting written by Institute for Operations Research and the Management Sciences. National Meeting and published by . This book was released on 2009 with total page 644 pages. Available in PDF, EPUB and Kindle. Book excerpt:
