Download or read book Analysis of Breast Cancer Screening Policies Using Partially Observable Markov Decision Processes written by Mucahit Cevik and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this dissertation, we study three aspects of the breast cancer screening problem: impact of breast density and supplemental screenings, screening in resource-restricted settings and racial disparities in breast cancer outcomes. We first analyze the impacts of breast density and supplemental tests on breast cancer screening policies. We formulate the breast cancer screening problem using a discrete-time partially observable Markov decision process (POMDP) model. The state space of our model is composed of the patient's health states and the breast density states. At each decision epoch, the physician first decides whether or not the patient should undergo mammography screening, and then uses mammography result to decide whether or not to follow up with supplemental screening. Our numerical study demonstrates that incremental benefit of supplemental tests over digital mammography is rather limited; in particular, patients with high breast cancer risk should be recommended more frequent mammography screenings instead of supplemental tests. Next, we investigate the optimal allocation of limited mammography resources to screen a population. We propose a constrained POMDP model that maximizes total expected quality-adjusted life years of the patients when they are allowed only a limited number of mammography screenings. We use a variable resolution grid-based approximation scheme to convert the constrained POMDP model into a mixed-integer linear program and conduct several numerical experiments using breast cancer epidemiology data. We observe that as mammography screening capacity decreases, patients in the 40-49 age group should be given the least priority with respect to screening. We further find that efficient allocation of available resources between patients with different risk levels leads to significant quality-adjusted life year gains, especially for the patients with higher breast cancer risk. Finally, we consider race as a risk factor for breast cancer and investigate the contributing factors leading to higher breast cancer mortality among black women. We modify the University of Wisconsin Breast Cancer Simulation model to obtain race-specific models and analyze the differences in disease natural history, treatment utilization and mammography uptake. Our findings indicate that targeted prevention and detection strategies that go beyond equalizing access to mammography may be needed to eliminate racial disparities.
Download or read book Markov Decision Process Approach to Strategize National Breast Cancer Screening Policy in Data limited Settings written by Vijeta Deshpande and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Early diagnosis is a promising strategy to reduce premature mortalities and for optimal use of resources. But the absence of mathematical models specific to the data settings in LMIC's impedes the construction of economic analysis necessary for decision-makers in the development of cancer control programs. This thesis presents a new methodology for parameterizing the natural history model of breast cancer based on data availabilities in low and middle income countries, and formulation of a control optimization problem to find the optimal screening schedule for mammography screening, solved using dynamic programming. As harms and benefits are known to increase with the increase in the number of lifetime screens, the trade-off was modeled by formulating the immediate reward as a function of false positives and life-years saved. The method presented in thesis will provide optimal screening schedules for multiple scenarios of Willingness to Pay (numeric value assigned for each life-year lived), including the resulting total number of lifetime screens per person, which can help decision-makers evaluate current resource availabilities or plan future resource needs for implementation.
Download or read book Partially Observable Markov Decision Processes for Prostate Cancer Screening written by Jingyu Zhang and published by . This book was released on 2011 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Optimizing Cancer Screening with POMDPs written by Panayiotis Petousis and published by . This book was released on 2019 with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt: Current clinical decision-making relies heavily both upon the experience of a physician and the recommendations of evidence-based practice guidelines, the latter often informed by population-level policies. Yet with the heightened complexity of patient care given newer types of data and longitudinal observations (e.g., from the electronic health record, EHR), as well as the goal of more individually-tailored healthcare, medical decision-making is increasingly complicated. This issue is particularly true in cancer with emergent techniques for early detection and personalized treatment. This research establishes an informatics-based framework to inform optimal cancer screening through sequential decision-making methods. This dissertation develops tools to formulate a partially observable Markov decision process (POMDP) model, enabling each component to be learned from a dataset: dynamic Bayesian networks (DBNs) are embedded in the POMDP learning process to estimate transition and observations probabilities; inverse reinforcement learning is used to learn a reward function from experts' prior decisions, and risk prediction models are employed to compute individualized initial beliefs about disease state. The result is a comprehensive approach to implementing sequential decision making agents. These methods are validated using large datasets from lung and breast cancer screening efforts, demonstrating the potential to help tailor and improve early cancer prediction while reducing false positive tests.
Download or read book Personalizing the Screening Decisions for Patients with Multiple Chronic Conditions written by Ali Hjaar and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Clinical practice guidelines do not sufficiently address the needs of patients with multiple chronic conditions (MCC) as these guidelines focus on single disease management and ignore unique patient-specific conditions. As a result, a nonpersonalized approach for the management of patients with MCC leads to adverse events and increases the financial burden on the health care system as over 150 million Americans experience MCC. Therefore, the focus of this dissertation is to develop methods to personalize the screening decisions for patients with MCC. Namely, we use simulation and optimization models to evaluate the impact of the national screening strategies and find the optimal screening policy for patients with MCC given their personal risk, life expectancy, and budget constraints. This dissertation is divided into 5 chapters. In Chapter 1, we review the impact of multiple chronic conditions on the health care system. In Chapter 2, we provide a literature review about completely and partially observable Markov decision processes and the use of simulation and mathematical models in improving screening guidelines. Moreover, we review the medical literature related to tailoring the clinical practice guidelines for patients with MCC. In Chapter 3, we investigate the breast cancer screening decision problem for women with Down syndrome as they have a lower breast cancer risk and significantly lower life expectancies than women without Down syndrome. Therefore, it is not clear whether mammography screening strategies used for women without Down syndrome would benefit women with Down syndrome in the same way. We use simulation modeling to estimate the potential harms and benefits of mammography screening for women with Down syndrome. We consider various mammography screening strategies that included annual, biennial, triennial, and one-time digital mammography screenings during the ages 40-74. We estimate numbers of mammograms, false-positives, benign biopsies, breast cancer deaths prevented, and life-years gained per 1000 screened women when compared with no screening. In average-risk women 50-74, biennial screening incurred 146 mammograms, 13 false-positive mammograms, and 1.9 benign biopsies per one life-year gained compared with no screening. In women with Down syndrome, the same screening strategy incurred 1,670 mammograms, 156 false-positive mammograms, and 22 benign biopsies per one life-year gained compared with no screening. The harm/benefit ratio varied for other screening strategies, and was most favorable for one-time screening at age 50, which incurred 1,230 mammograms, 112 false-positive mammograms, and 16 benign biopsies per one life-year gained compared with no screening. We find that the harm/benefit ratios for various mammography screening strategies in women with Down syndrome are not as favorable as those for average-risk women. That is, the benefit of screening mammography for women with Down syndrome is less pronounced due to lower breast cancer risk and shorter life expectancy. In Chapter 4, we focus on personalizing the screening decisions for patients with MCC. For this purpose, we develop a stochastic modeling framework and provide an exact solution algorithm. We consider the optimal management of screening decisions for an index disease (e.g., breast cancer, colorectal cancer, HIV, etc.) while accounting for the existence of a chronic condition (e.g., hypertension, diabetes, Alzheimer's disease, etc.). Our modeling framework is particularly useful for cases where the chronic condition affects the risk of the index disease. In a case study using real breast cancer epidemiology data, we demonstrate how our modeling framework can be used to personalize breast cancer screening for women with type 2 diabetes. In addition to providing a personalized breast cancer screening schedule for women with diabetes, we find some important policy insights that were not previously recognized by the medical community. More specifically, we find that compared to women with no diabetes, women with diabetes should be screened less aggressively, but screening should end at similar ages. We also find that adherence to the optimal screening policy is more crucial for women with diabetes compared to women with no diabetes. Our main insight on screening recommendations also has important resource implications as it leads to fewer screening mammograms. That is, compared to the current national breast cancer screening guidelines, the optimal breast cancer screening policy for women with diabetes could save the health care system approximately 2.6 million mammograms annually which translates to $405 million of annual cost savings. Due to the increasing cost of healthcare, financial and limited resources constraints ought to be considered. Hence, we extend our modeling framework in Chapter 5 to examine the impact of the costs related to breast cancer screening, diagnosis, and treatment on the optimal screening policy for women with no diabetes, women with pre-diabetes, and women with diabetes. We use willingness-to-pay (WTP) ratios to evaluate the costs related to screening, diagnosis, and treatment costs in terms of QALYs. Our numerical results show that the optimal screening policy is greatly affected by the WTP ratios.
Download or read book Modern Trends in Controlled Stochastic Processes written by Alexey Piunovskiy and published by Springer Nature. This book was released on 2021-06-04 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents state-of-the-art solution methods and applications of stochastic optimal control. It is a collection of extended papers discussed at the traditional Liverpool workshop on controlled stochastic processes with participants from both the east and the west. New problems are formulated, and progresses of ongoing research are reported. Topics covered in this book include theoretical results and numerical methods for Markov and semi-Markov decision processes, optimal stopping of Markov processes, stochastic games, problems with partial information, optimal filtering, robust control, Q-learning, and self-organizing algorithms. Real-life case studies and applications, e.g., queueing systems, forest management, control of water resources, marketing science, and healthcare, are presented. Scientific researchers and postgraduate students interested in stochastic optimal control,- as well as practitioners will find this book appealing and a valuable reference.
Download or read book A Partially Observable Markov Decision for Optimal Design of Surveillance Policies for Bladder Cancer written by Yuan Zhang and published by . This book was released on 2012 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Markov Decision Processes in Practice written by Richard J. Boucherie and published by Springer. This book was released on 2017-03-10 with total page 563 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents classical Markov Decision Processes (MDP) for real-life applications and optimization. MDP allows users to develop and formally support approximate and simple decision rules, and this book showcases state-of-the-art applications in which MDP was key to the solution approach. The book is divided into six parts. Part 1 is devoted to the state-of-the-art theoretical foundation of MDP, including approximate methods such as policy improvement, successive approximation and infinite state spaces as well as an instructive chapter on Approximate Dynamic Programming. It then continues with five parts of specific and non-exhaustive application areas. Part 2 covers MDP healthcare applications, which includes different screening procedures, appointment scheduling, ambulance scheduling and blood management. Part 3 explores MDP modeling within transportation. This ranges from public to private transportation, from airports and traffic lights to car parking or charging your electric car . Part 4 contains three chapters that illustrates the structure of approximate policies for production or manufacturing structures. In Part 5, communications is highlighted as an important application area for MDP. It includes Gittins indices, down-to-earth call centers and wireless sensor networks. Finally Part 6 is dedicated to financial modeling, offering an instructive review to account for financial portfolios and derivatives under proportional transactional costs. The MDP applications in this book illustrate a variety of both standard and non-standard aspects of MDP modeling and its practical use. This book should appeal to readers for practitioning, academic research and educational purposes, with a background in, among others, operations research, mathematics, computer science, and industrial engineering.
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 2018-02-02 with total page 487 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.
Download or read book Operations Research Applications in Health Care Management written by Cengiz Kahraman and published by Springer. This book was released on 2017-12-08 with total page 596 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a comprehensive reference guide to operations research theory and applications in health care systems. It provides readers with all the necessary tools for solving health care problems. The respective chapters, written by prominent researchers, explain a wealth of both basic and advanced concepts of operations research for the management of operating rooms, intensive care units, supply chain, emergency medical service, human resources, lean health care, and procurement. To foster a better understanding, the chapters include relevant examples or case studies. Taken together, they form an excellent reference guide for researchers, lecturers and postgraduate students pursuing research on health care management problems. The book presents a dynamic snapshot on the field that is expected to stimulate new directions and stimulate new ideas and developments.
Download or read book The Routledge Companion to Production and Operations Management written by Martin K. Starr and published by Taylor & Francis. This book was released on 2017-03-27 with total page 712 pages. Available in PDF, EPUB and Kindle. Book excerpt: This remarkable volume highlights the importance of Production and Operations Management (POM) as a field of study and research contributing to substantial business and social growth. The editors emphasize how POM works with a range of systems—agriculture, disaster management, e-commerce, healthcare, hospitality, military systems, not-for-profit, retail, sports, sustainability, telecommunications, and transport—and how it contributes to the growth of each. Martin K. Starr and Sushil K. Gupta gather an international team of experts to provide researchers and students with a panoramic vision of the field. Divided into eight parts, the book presents the history of POM, and establishes the foundation upon which POM has been built while also revisiting and revitalizing topics that have long been essential. It examines the significance of processes and projects to the fundamental growth of the POM field. Critical emerging themes and new research are examined with open minds and this is followed by opportunities to interface with other business functions. Finally, the next era is discussed in ways that combine practical skill with philosophy in its analysis of POM, including traditional and nontraditional applications, before concluding with the editors’ thoughts on the future of the discipline. Students of POM will find this a comprehensive, definitive resource on the state of the discipline and its future directions.
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.
Download or read book Integration of Machine Learning and Computer Simulation in Solving Complex Physiological and Medical Questions written by Nicole Y. K. Li-Jessen and published by Frontiers Media SA. This book was released on 2022-08-01 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Industrial Engineering in the Internet of Things World written by Fethi Calisir and published by Springer Nature. This book was released on 2021-08-07 with total page 486 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers extended versions of the best papers presented at the Global Joint Conference on Industrial Engineering and Its Application Areas (GJCIE), organized virtually on August 14–15, 2020, by Istanbul Technical University. It covers a wide range of topics, including decision analysis, supply chain management, systems modelling and quality control. Further, special emphasis is placed on cutting-edge applications of industrial Internet-of-Things. Technological, economic and business challenges are discussed in detail, presenting effective strategies that can be used to modernize current structures, eliminating the barriers that are keeping industries from taking full advantage of IoT technologies. The book offers an important link between technological research and industry best practices, and covers various disciplinary areas such as manufacturing, healthcare and service engineering, among others.
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 2018-02-02 with total page 430 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.
Download or read book Secondary Analysis of Electronic Health Records written by MIT Critical Data and published by Springer. This book was released on 2016-09-09 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care. It formulates a more complete lexicon of evidence-based recommendations and support shared, ethical decision making by doctors with their patients. Diagnostic and therapeutic technologies continue to evolve rapidly, and both individual practitioners and clinical teams face increasingly complex ethical decisions. Unfortunately, the current state of medical knowledge does not provide the guidance to make the majority of clinical decisions on the basis of evidence. The present research infrastructure is inefficient and frequently produces unreliable results that cannot be replicated. Even randomized controlled trials (RCTs), the traditional gold standards of the research reliability hierarchy, are not without limitations. They can be costly, labor intensive, and slow, and can return results that are seldom generalizable to every patient population. Furthermore, many pertinent but unresolved clinical and medical systems issues do not seem to have attracted the interest of the research enterprise, which has come to focus instead on cellular and molecular investigations and single-agent (e.g., a drug or device) effects. For clinicians, the end result is a bit of a “data desert” when it comes to making decisions. The new research infrastructure proposed in this book will help the medical profession to make ethically sound and well informed decisions for their patients.
Download or read book Artificial Intelligence in Health written by Fernando Koch and published by Springer. This book was released on 2019-02-20 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed post-conference proceedings of the First International Workshop on Artificial Intelligence in Health, AIH 2018, in Stockholm, Sweden, in July 2018. This workshop consolidated the workshops CARE, KRH4C and AI4HC into a single event. The 18 revised full papers included in this volume were carefully selected from the 26 papers accepted for presentation out of 42 initial submissions. The papers present AI technologies with medical applications and are organized in three tracks: agents in healthcare; data science and decision systems in medicine; and knowledge management in healthcare.
