Download or read book Applications of Stochastic Modeling and Data Analytics Techniques in Healthcare Decision Making written by Ozden Onur Dalgic and published by . This book was released on 2017 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: We present approaches utilizing aspects of data analytics and stochastic modeling techniques and applied to various areas in healthcare. In general, the thesis has composed of three major components. Firtsly, we propose a comparison analysis between two of the very well-known infectious disease modeling techniques to derive effective vaccine allocation strategies. This study, has emerged from the fact that individuals are prioritized based on their risk profiles when allocating limited vaccine stocks during an influenza pandemic. Computationally expensive but realistic agent-based simulations and fast but stylized compartmental models are typically used to derive effective vaccine allocation strategies. A detailed comparison of these two approaches, however, is often omitted. We derive age-specific vaccine allocation strategies to mitigate a pandemic influenza outbreak in Seattle by applying derivative-free optimization to an agent-based simulation and also to a compartmental model. We compare the strategies derived by these two approaches under various infection aggressiveness and vaccine coverage scenarios. We observe that both approaches primarily vaccinate school children, however they may allocate the remaining vaccines in different ways. The vaccine allocation strategies derived by using the agent-based simulation are associated with up to 70% decrease in total cost and 34% reduction in the number of infections compared to the strategies derived by the compartmental model. Nevertheless, the latter approach may still be competitive for very low and/or very high infection aggressiveness. Our results provide insights about the possible differences between the vaccine allocation strategies derived by using agent-based simulations and those derived by using compartmental models. Secondly, we introduce a novel and holistic scheme to capture the gradual amyotrophic lateral sclerosis progression based on the critical events referred as tollgates. Amyotrophic lateral sclerosis is neuro-degenerative and terminal disease. Patients with amyotrophic lateral sclerosis lose control of voluntary movements over time due to continuous degeneration of motor neurons. Using a comprehensive longitudinal dataset from Mayo Clinic's ALS Clinic in Rochester, MN, we characterize the progression through tollgates at the body segment (e.g., arm, leg, speech, swallowing, breathing) and patient levels over time. We describe how the progression based on the followed tollgate pathways varies among patients and ultimately, how this type of progression characterization may be utilized for further studies. Kaplan-Meier analysis are conducted to derive the probability of passing each tollgate over time. We observe that, in each body segment, the majority of the patients have their abilities affected or worse (Level1) at the first visit. Especially, the proportion of patients at higher tollgate levels is larger for arm and leg segments compared to others. For each segment, we derive the over-time progression pathways of patients in terms of the reached tollgates. Tollgates towards later visits show a great diversity among patients who were at the same tollgate level at the first clinic visit. The proposed tollgate mechanism well captures the variability among patients and the history plays a role on when patients reach tollgates. We suggest that further and comprehensive studies should be conducted to observe the whole effect of the history in the future progression. Thirdly, based on the fact that many available databases may not have detailed medical records to derive the necessary data, we propose a classification-based approach to estimate the tollgate data using ALSFRS-R scores which are available in most databases. We observed that tollgates are significantly associated with the ALSFRS-R scores. Multiclass classification techniques are commonly used in such problem; however, traditional classification techniques are not applicable to the problem of finding the tollgates due to the constraint of that a patients' tollgates under a specific segment for multiple visit should be non-decreasing over time. Therefore, we propose two approaches to achieve a multi-class estimation in a non-decreasing manner given a classification method. While the first approach fixes the class estimates of observation in a sequential manner, the second approach utilizes a mixed integer programming model to estimate all the classes of a patients' observations. We used five different multi-class classification techniques to be employed by both of the above implementations. Thus, we investigate the performance of classification model employed under both approaches for each body segment.

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.

Download or read book Stochastic Modeling And Analytics In Healthcare Delivery Systems written by Jingshan Li and published by World Scientific. This book was released on 2017-09-22 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, there has been an increased interest in the field of healthcare delivery systems. Scientists and practitioners are constantly searching for ways to improve the safety, quality and efficiency of these systems in order to achieve better patient outcome.This book focuses on the research and best practices in healthcare engineering and technology assessment. With contributions from researchers in the fields of healthcare system stochastic modeling, simulation, optimization and management, this is a valuable read.

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.

Download or read book Stochastic Modeling and Decision Making in Two Healthcare Applications written by Pengyi Shi and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Delivering health care services in an efficient and effective way has become a great challenge for many countries due to the aging population worldwide, rising health expenses, and increasingly complex healthcare delivery systems. It is widely recognized that models and analytical tools can aid decision-making at various levels of the healthcare delivery process, especially when decisions have to be made under uncertainty. This thesis employs stochastic models to improve decision-making under uncertainty in two specific healthcare settings: inpatient flow management and infectious disease modeling. In Part I of this thesis, we study patient flow from the emergency department (ED) to hospital inpatient wards. This line of research aims to develop insights into effective inpatient flow management to reduce the waiting time for admission to inpatient wards from the ED. Delayed admission to inpatient wards, also known as ED boarding, has been identified as a key contributor to ED overcrowding and is a big challenge for many hospitals. Part I consists of three main chapters. In Chapter 2 we present an extensive empirical study of the inpatient department at our collaborating hospital. Motivated by this empirical study, in Chapter 3 we develop a high fidelity stochastic processing network model to capture inpatient flow with a focus on the transfer process from the ED to the wards. In Chapter 4 we devise a new analytical framework, two-time-scale analysis, to predict time-dependent performance measures for some simplified versions of our proposed model. We explore both exact Markov chain analysis and diffusion approximations. Part I of the thesis makes contributions in three dimensions. First, we identify several novel features that need to be built into our proposed stochastic network model. With these features, our model is able to capture inpatient flow dynamics at hourly resolution and reproduce the empirical time-dependent performance measures, whereas traditional time-varying queueing models fail to do so. These features include unconventional non-i.i.d. (independently and identically distributed) service times, an overflow mechanism, and allocation delays. Second, our two-time-scale framework overcomes a number of challenges faced by existing analytical methods in analyzing models with these novel features. These challenges include time-varying arrivals and extremely long service times. Third, analyzing the developed stochastic network model generates a set of useful managerial insights, which allow hospital managers to (i) identify strategies to reduce the waiting time and (ii) evaluate the trade-off between the benefit of reducing ED congestion and the cost from implementing certain policies. In particular, we identify early discharge policies that can eliminate the excessively long waiting times for patients requesting beds in the morning. In Part II of the thesis, we model the spread of influenza pandemics with a focus on identifying factors that may lead to multiple waves of outbreak. This line of research aims to provide insights and guidelines to public health officials in pandemic preparedness and response. In Chapter 6 we evaluate the impact of seasonality and viral mutation on the course of an influenza pandemic. In Chapter 7 we evaluate the impact of changes in social mixing patterns, particularly mass gatherings and holiday traveling, on the disease spread. In Chapters 6 and 7 we develop agent-based simulation models to capture disease spread across both time and space, where each agent represents an individual with certain socio-demographic characteristics and mixing patterns. The important contribution of our models is that the viral transmission characteristics and social contact patterns, which determine the scale and velocity of the disease spread, are no longer static. Simulating the developed models, we study the effect of the starting season of a pandemic, timing and degree of viral mutation, and duration and scale of mass gatherings and holiday traveling on the disease spread. We identify possible scenarios under which multiple outbreaks can occur during an influenza pandemic. Our study can help public health officials and other decision-makers predict the entire course of an influenza pandemic based on emerging viral characteristics at the initial stage, determine what data to collect, foresee potential multiple waves of attack, and better prepare response plans and intervention strategies, such as postponing or cancelling public gathering events.

Download or read book Advanced Prognostic Predictive Modelling in Healthcare Data Analytics written by Sudipta Roy and published by Springer Nature. This book was released on 2021-04-22 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses major technical advancements and research findings in the field of prognostic modelling in healthcare image and data analysis. The use of prognostic modelling as predictive models to solve complex problems of data mining and analysis in health care is the feature of this book. The book examines the recent technologies and studies that reached the practical level and becoming available in preclinical and clinical practices in computational intelligence. The main areas of interest covered in this book are highest quality, original work that contributes to the basic science of processing, analysing and utilizing all aspects of advanced computational prognostic modelling in healthcare image and data analysis.

Download or read book Applied Stochastic Models And Data Analysis Proceedings Of The Fifth International Symposium On Asmda written by Valderrama M J and published by #N/A. This book was released on 1991-03-29 with total page 672 pages. Available in PDF, EPUB and Kindle. Book excerpt: As with previous symposiums, the main objective of the Sixth International Symposium is to publish papers (of both technical and practical nature) to present new findings uncovered by theoretical results which may have the potential to contribute solutions to real-life problems. With this objective in mind, this collection of papers aims to serve as an interface between stochastic modeling and data analysis as well as their applications to the problems we face in the various fields. The papers first focused on the theory, application and interaction between stochastic models and data analysis. The results and their applications to the problems we face in the fields of economics, finance and insurance, management, marketing, health sciences, production and engineering are then explored.

Download or read book Data Driven Remaining Useful Life Prognosis Techniques written by Xiao-Sheng Si and published by Springer. This book was released on 2017-01-20 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces data-driven remaining useful life prognosis techniques, and shows how to utilize the condition monitoring data to predict the remaining useful life of stochastic degrading systems and to schedule maintenance and logistics plans. It is also the first book that describes the basic data-driven remaining useful life prognosis theory systematically and in detail. The emphasis of the book is on the stochastic models, methods and applications employed in remaining useful life prognosis. It includes a wealth of degradation monitoring experiment data, practical prognosis methods for remaining useful life in various cases, and a series of applications incorporated into prognostic information in decision-making, such as maintenance-related decisions and ordering spare parts. It also highlights the latest advances in data-driven remaining useful life prognosis techniques, especially in the contexts of adaptive prognosis for linear stochastic degrading systems, nonlinear degradation modeling based prognosis, residual storage life prognosis, and prognostic information-based decision-making.

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.

Download or read book Big Data Analytics for Healthcare written by Pantea Keikhosrokiani and published by Academic Press. This book was released on 2022-05-19 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data Analytics and Medical Information Systems presents the valuable use of artificial intelligence and big data analytics in healthcare and medical sciences. It focuses on theories, methods and approaches in which data analytic techniques can be used to examine medical data to provide a meaningful pattern for classification, diagnosis, treatment, and prediction of diseases. The book discusses topics such as theories and concepts of the field, and how big medical data mining techniques and applications can be applied to classification, diagnosis, treatment, and prediction of diseases. In addition, it covers social, behavioral, and medical fake news analytics to prevent medical misinformation and myths. It is a valuable resource for graduate students, researchers and members of biomedical field who are interested in learning more about analytic tools to support their work. Presents theories, methods and approaches in which data analytic techniques are used for medical data Brings practical information on how to use big data for classification, diagnosis, treatment, and prediction of diseases Discusses social, behavioral, and medical fake news analytics for medical information systems

Download or read book Intelligent Techniques for Predictive Data Analytics written by Neha Singh and published by John Wiley & Sons. This book was released on 2024-07-30 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive resource covering tools and techniques used for predictive analytics with practical applications across various industries Intelligent Techniques for Predictive Data Analytics provides an in-depth introduction of the tools and techniques used for predictive analytics, covering applications in cyber security, network security, data mining, and machine learning across various industries. Each chapter offers a brief introduction on the subject to make the text accessible regardless of background knowledge. Readers will gain a clear understanding of how to use data processing, classification, and analysis to support strategic decisions, such as optimizing marketing strategies and customer relationship management and recommendation systems, improving general business operations, and predicting occurrence of chronic diseases for better patient management. Traditional data analytics uses dashboards to illustrate trends and outliers, but with large data sets, this process is labor-intensive and time-consuming. This book provides everything readers need to save time by performing deep, efficient analysis without human bias and time constraints. A section on current challenges in the field is also included. Intelligent Techniques for Predictive Data Analytics covers sample topics such as: Models to choose from in predictive modeling, including classification, clustering, forecast, outlier, and time series models Price forecasting, quality optimization, and insect and disease plant and monitoring in agriculture Fraud detection and prevention, credit scoring, financial planning, and customer analytics Big data in smart grids, smart grid analytics, and predictive smart grid quality monitoring, maintenance, and load forecasting Management of uncertainty in predictive data analytics and probable future developments in the field Intelligent Techniques for Predictive Data Analytics is an essential resource on the subject for professionals and researchers working in data science or data management seeking to understand the different models of predictive analytics, along with graduate students studying data science courses and professionals and academics new to the field.

Download or read book Practical Predictive Analytics and Decisioning Systems for Medicine written by Gary D. Miner and published by Academic Press. This book was released on 2014-09-27 with total page 1111 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the advent of electronic medical records years ago and the increasing capabilities of computers, our healthcare systems are sitting on growing mountains of data. Not only does the data grow from patient volume but the type of data we store is also growing exponentially. Practical Predictive Analytics and Decisioning Systems for Medicine provides research tools to analyze these large amounts of data and addresses some of the most pressing issues and challenges where data integrity is compromised: patient safety, patient communication, and patient information. Through the use of predictive analytic models and applications, this book is an invaluable resource to predict more accurate outcomes to help improve quality care in the healthcare and medical industries in the most cost–efficient manner.Practical Predictive Analytics and Decisioning Systems for Medicine provides the basics of predictive analytics for those new to the area and focuses on general philosophy and activities in the healthcare and medical system. It explains why predictive models are important, and how they can be applied to the predictive analysis process in order to solve real industry problems. Researchers need this valuable resource to improve data analysis skills and make more accurate and cost-effective decisions. Includes models and applications of predictive analytics why they are important and how they can be used in healthcare and medical research Provides real world step-by-step tutorials to help beginners understand how the predictive analytic processes works and to successfully do the computations Demonstrates methods to help sort through data to make better observations and allow you to make better predictions

Download or read book Using Predictive Analytics to Improve Healthcare Outcomes written by John W. Nelson and published by John Wiley & Sons. This book was released on 2021-07-09 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using Predictive Analytics to Improve Healthcare Outcomes Winner of the American Journal of Nursing (AJN) Informatics Book of the Year Award 2021! Discover a comprehensive overview, from established leaders in the field, of how to use predictive analytics and other analytic methods for healthcare quality improvement. Using Predictive Analytics to Improve Healthcare Outcomes delivers a 16-step process to use predictive analytics to improve operations in the complex industry of healthcare. The book includes numerous case studies that make use of predictive analytics and other mathematical methodologies to save money and improve patient outcomes. The book is organized as a “how-to” manual, showing how to use existing theory and tools to achieve desired positive outcomes. You will learn how your organization can use predictive analytics to identify the most impactful operational interventions before changing operations. This includes: A thorough introduction to data, caring theory, Relationship-Based Care®, the Caring Behaviors Assurance System©, and healthcare operations, including how to build a measurement model and improve organizational outcomes. An exploration of analytics in action, including comprehensive case studies on patient falls, palliative care, infection reduction, reducing rates of readmission for heart failure, and more—all resulting in action plans allowing clinicians to make changes that have been proven in advance to result in positive outcomes. Discussions of how to refine quality improvement initiatives, including the use of “comfort” as a construct to illustrate the importance of solid theory and good measurement in adequate pain management. An examination of international organizations using analytics to improve operations within cultural context. Using Predictive Analytics to Improve Healthcare Outcomes is perfect for executives, researchers, and quality improvement staff at healthcare organizations, as well as educators teaching mathematics, data science, or quality improvement. Employ this valuable resource that walks you through the steps of managing and optimizing outcomes in your clinical care operations.

Download or read book Healthcare Analytics written by Hui Yang and published by . This book was released on 2016 with total page 602 pages. Available in PDF, EPUB and Kindle. Book excerpt: Features of statistical and operational research methods and tools being used to improve the healthcare industry With a focus on cutting-edge approaches to the quickly growing field of healthcare, Healthcare Analytics: From Data to Knowledge to Healthcare Improvement provides an integrated and comprehensive treatment on recent research advancements in data-driven healthcare analytics in an effort to provide more personalized and smarter healthcare services. Emphasizing data and healthcare analytics from an operational management and statistical perspective, the book details how analytical methods and tools can be utilized to enhance healthcare quality and operational efficiency. Organized into two main sections, Part I features biomedical and health informatics and specifically addresses the analytics of genomic and proteomic data; physiological signals from patient-monitoring systems; data uncertainty in clinical laboratory tests; predictive modeling; disease modeling for sepsis; and the design of cyber infrastructures for early prediction of epidemic events. Part II focuses on healthcare delivery systems, including system advances for transforming clinic workflow and patient care; macro analysis of patient flow distribution; intensive care units; primary care; demand and resource allocation; mathematical models for predicting patient readmission and postoperative outcome; physician-patient interactions; insurance claims; and the role of social media in healthcare. Healthcare Analytics: From Data to Knowledge to Healthcare Improvement also features: - Contributions from well-known international experts who shed light on new approaches in this growing area - Discussions on contemporary methods and techniques to address the handling of rich and large-scale healthcare data as well as the overall optimization of healthcare system operations - Numerous real-world examples and case studies that emphasize the vast potential of statistical and operational research tools and techniques to address the big data environment within the healthcare industry - Plentiful applications that showcase analytical methods and tools tailored for successful healthcare systems modeling and improvement The book is an ideal reference for academics and practitioners in operations research, management science, applied mathematics, statistics, business, industrial and systems engineering, healthcare systems, and economics. Healthcare Analytics: From Data to Knowledge to Healthcare Improvement is also appropriate for graduate-level courses typically offered within operations research, industrial engineering, business, and public health departments. HUI YANG, PhD, is Associate Professor in the Harold and Inge Marcus Department of Industrial and Manufacturing Engineering at The Pennsylvania State University. His research interests include sensor-based modeling and analysis of complex systems for process monitoring/control; system diagnostics/ prognostics; quality improvement; and performance optimization with special focus on nonlinear stochastic dynamics and the resulting chaotic, recurrence, self-organizing behaviors. EVA K. LEE, PhD, is Professor in the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology, Director of the Center for Operations Research in Medicine and HealthCare, and Distinguished Scholar in Health System, Health Systems Institute at both Emory University School of Medicine and Georgia Institute of Technology. Her research interests include health-risk prediction; early disease prediction and diagnosis; optimal treatment strategies and drug delivery; healthcare outcome analysis and treatment prediction; public health and medical preparedness; large-scale healthcare/medical decision analysis and quality improvement; clinical translational.

Download or read book Knowledge Modelling and Big Data Analytics in Healthcare written by Mayuri Mehta and published by . This book was released on 2021-11 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book focuses on automated analytical techniques for healthcare applications used to extract knowledge from a large amount of data. It brings together a variety of different aspects of the healthcare system and aids in the decision-making processes for healthcare professionals. Knowledge Modelling and Big Data Analytics in Healthcare: Advances and Applications connects four contemporary areas of research rarely brought together in one book: artificial intelligence, big data analytics, knowledge modelling, and healthcare. It presents state-of-the-art research from the healthcare sector, including research on medical imaging, healthcare analysis, and the applications of artificial intelligence in drug discovery. This book is intended for data scientists, academicians, and industry professionals in the healthcare sector"--

Download or read book Decision Making in Healthcare Systems written by Tofigh Allahviranloo and published by Springer Nature. This book was released on 2023-12-31 with total page 441 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book chooses the topic which is due to the editors' experience in modeling projects in healthcare systems. Also, the transfer of experiences is the reason why mathematical modeling and decision making in the field of health are not given much attention. To this end, the new aspect of this book is the lack of reference needed to carry out projects in the field of health for researchers whose main expertise is not modeling. Students of health, mathematics, management, and industrial engineering fields are in the direct readership with this book. Different projects in the field of healthcare systems can use the topics presented in different chapters mentioned in this book.

Download or read book Data Guided Healthcare Decision Making written by Ramalingam Shanmugam and published by Cambridge University Press. This book was released on 2023-05-31 with total page 529 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book effectively exposes and illustrates the ideas and tools for optimal healthcare decisions taken from evidence.