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Flexible Bayesian Models For Medical Diagnostic Data
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Book Synopsis Flexible Bayesian Models for Medical Diagnostic Data by : Vanda Inácio de Carvalho
Download or read book Flexible Bayesian Models for Medical Diagnostic Data written by Vanda Inácio de Carvalho and published by Chapman and Hall/CRC. This book was released on 2016-05-15 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Offering a detailed and careful explanation of the methods, this book delineates Bayesian non parametric techniques to be used in health care and the statistical evaluation of diagnostic tests to determine accuracy before mass use in practice. Unique to these methods is the incorporation of prior information and elimination of subjective beliefs and asymptotic results. It includes examples such as ROC curves and ROC surfaces estimation, modeling of multivariate diagnostic data, absence of a perfect test, ROC regression methodology, and sample size determination.
Book Synopsis Bayesian Biostatistics and Diagnostic Medicine by : Lyle D. Broemeling
Download or read book Bayesian Biostatistics and Diagnostic Medicine written by Lyle D. Broemeling and published by CRC Press. This book was released on 2007-07-12 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: There are numerous advantages to using Bayesian methods in diagnostic medicine, which is why they are employed more and more today in clinical studies. Exploring Bayesian statistics at an introductory level, Bayesian Biostatistics and Diagnostic Medicine illustrates how to apply these methods to solve important problems in medicine and biology.
Book Synopsis Bayesian and grAphical Models for Biomedical Imaging by : M. Jorge Cardoso
Download or read book Bayesian and grAphical Models for Biomedical Imaging written by M. Jorge Cardoso and published by Springer. This book was released on 2014-09-22 with total page 139 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the First International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2014, held in Cambridge, MA, USA, in September 2014 as a satellite event of the 17th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2014. The 11 revised full papers presented were carefully reviewed and selected from numerous submissions with a key aspect on probabilistic modeling applied to medical image analysis. The objectives of this workshop compared to other workshops, e.g. machine learning in medical imaging, have a stronger mathematical focus on the foundations of probabilistic modeling and inference. The papers highlight the potential of using Bayesian or random field graphical models for advancing scientific research in biomedical image analysis or for the advancement of modeling and analysis of medical imaging data.
Book Synopsis Advanced Bayesian Methods for Medical Test Accuracy by : Lyle D. Broemeling
Download or read book Advanced Bayesian Methods for Medical Test Accuracy written by Lyle D. Broemeling and published by CRC Press. This book was released on 2016-04-19 with total page 482 pages. Available in PDF, EPUB and Kindle. Book excerpt: Useful in many areas of medicine and biology, Bayesian methods are particularly attractive tools for the design of clinical trials and diagnostic tests, which are based on established information, usually from related previous studies. Advanced Bayesian Methods for Medical Test Accuracy begins with a review of the usual measures such as specificity
Book Synopsis Bayesian Models for Screening and Diagnosis of Pulmonary Disease by : Aneesh M. Anand
Download or read book Bayesian Models for Screening and Diagnosis of Pulmonary Disease written by Aneesh M. Anand and published by . This book was released on 2018 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pulmonary and respiratory diseases comprise a large proportion of the global disease burden, responsible for both mortality and disability, with the most common ailments being asthma, chronic obstructive pulmonary disorder (COPD), and allergic rhinitis (AR). This burden is especially concentrated in the developing world, where resources for diagnosing these diseases are more limited. In India, COPD recently became the second leading cause of death. Health workers and many general practitioner doctors are not trained to diagnose pulmonary diseases, leading to high rates of misdiagnosis and underdiagnosis. Over the past six years, our group has been developing screening tools for pulmonary disease. We have developed a mobile toolkit that consists of an electronic stethoscope, an augmented reality peak flow meter, and an electronic questionnaire. Previously, logistic regression has been used for modeling pulmonary disease. However, logistic regression has certain important limitations: it does not model the problem causally, it isn’t very flexible, and it doesn’t handle missing data well. In this thesis, we propose a Bayesian framework for disease diagnosis in order to mitigate the issues with logistic regression. A Bayesian network model is presented for predicting the probability of specific pulmonary diseases. The network includes three layers consisting of Diseases, Risk Factors, and Symptoms. We then explored two different approaches to constructing the probability estimates and network parameters employed by the model. The first approach derived the network parameters using training data from a clinical study conducted at a pulmonary research hospital (Chest Research Foundation) located in Pune, India. Arriving patients at the clinic were tested using the MIT mobile toolkit and subsequently examined using a complete pulmonary function testing lab, from which a clinical diagnosis was obtained. Using this data, we built a Bayesian network which was able to accurately detect patients with asthma, COPD, allergic rhinitis, and other pulmonary diseases, with median AUC=0.9 for COPD, AUC=0.92 for Asthma, and AUC=0.89 for Allergic Rhinitis. The Bayesian model was shown to outperform logistic regression in the case of partially missing data. In our second approach, we constructed a Bayesian network with probabilities derived from expert opinions. We surveyed experienced pulmonologists and used their responses to parametrize our model. This model was also able to accurately classify patients with asthma, COPD, allergic rhinitis, and other pulmonary diseases. For future deployment in the field, our Bayesian diagnostic model has been integrated into Pulmonary Screener, a mobile phone application which is used to collect patient data and calculate probabilities of pulmonary disease. The current work has expanded the previous version of Pulmonary Screener by updating the model structure, improving the workflow, and making the application more intuitive for health professionals. Given the encouraging results of the generalized Bayesian model presented in this thesis, we believe this framework can be a promising approach for creating diagnostic and screening tools for many applications.
Book Synopsis Modeling in Medical Decision Making by : Giovanni Parmigiani
Download or read book Modeling in Medical Decision Making written by Giovanni Parmigiani and published by John Wiley & Sons. This book was released on 2002-03 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: Describes Bayesian inference, Monte Carlo simulation, utility theory and gives case studies of their use.
Book Synopsis Probabilistic Modeling in Bioinformatics and Medical Informatics by : Dirk Husmeier
Download or read book Probabilistic Modeling in Bioinformatics and Medical Informatics written by Dirk Husmeier and published by Springer Science & Business Media. This book was released on 2005-02 with total page 540 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written for researchers and students in statistics, machine learning, and the biological sciences. This book provides a self-contained introduction to the methodology of Bayesian networks. It offers both elementary tutorials as well as more advanced applications and case studies.
Book Synopsis Nonparametric Bayesian Inference in Biostatistics by : Riten Mitra
Download or read book Nonparametric Bayesian Inference in Biostatistics written by Riten Mitra and published by Springer. This book was released on 2015-07-25 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters cover: clinical trials, spatial inference, proteomics, genomics, clustering, survival analysis and ROC curve.
Book Synopsis Bayesian Data Analysis, Third Edition by : Andrew Gelman
Download or read book Bayesian Data Analysis, Third Edition written by Andrew Gelman and published by CRC Press. This book was released on 2013-11-01 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
Book Synopsis Revolutionizing Healthcare Treatment With Sensor Technology by : Das, Sima
Download or read book Revolutionizing Healthcare Treatment With Sensor Technology written by Das, Sima and published by IGI Global. This book was released on 2024-05-28 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: Traditional patient care and treatment approaches often lack the personalized and interactive elements necessary for effective healthcare delivery. This means that the healthcare industry must find innovative solutions to improve patient outcomes, enhance rehabilitation processes, and optimize resource utilization. There is a gap between the traditional approach and the need for innovation that highlights the importance of a comprehensive understanding of emerging technologies, including Kinect Sensor technology, and the potential to transform healthcare practices with this tech. Revolutionizing Healthcare Treatment With Sensor Technology addresses this critical need by thoroughly exploring how Kinect Sensor technology can revolutionize patient care and treatment methodologies. By repurposing and customizing Kinect Sensor for healthcare applications, this book showcases how depth-sensing cameras, infrared sensors, and advanced motion tracking can capture and interpret real-time patient movements and interactions. This book is ideal for healthcare professionals, hospital administrators, researchers, patients, caregivers, and healthcare technology developers seeking to leverage Kinect Sensor technology for enhanced healthcare delivery. Through detailed case studies and practical examples, experts can learn how to integrate Kinect Sensor into various medical settings to gain valuable insights into patients' physical capabilities, monitor their progress, and create personalized treatment plans.
Book Synopsis Visual Analysis of Bayesian Networks for Electronic Health Records by : Pacharmon Kaewprag
Download or read book Visual Analysis of Bayesian Networks for Electronic Health Records written by Pacharmon Kaewprag and published by . This book was released on 2018 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: Worldwide the amount of data generated by the medical community is staggering, and increasing dramatically. Using this data to improve patient care using analytics and machine learning is a huge and largely untapped opportunity. The most important medical data captured exist in patients' electronic health records (EHRs) which are maintained and utilized by health care providers. EHRs consist of rich and comprehensive patient-specific information from a large number of sources in different formats with heterogeneous data types. There are numerous challenges in attempting to apply existing analytic tools and methodologies to this data. Many features extracted from EHRs have dependent relationships - for example, “flu” and “high body temperature”. Bayesian networks, as one of the few modeling methodologies which capture feature dependence rather than assuming independence, provide a flexible foundation for modeling EHRs. However, existing Bayesian network learning methodologies produce models whose complexity makes them difficult for clinicians to utilize or even interpret. Therefore, better model visualization methodologies, as well as learning methods which produce models more amenable to simplification and summarization, are critical to making them interpretable and useful to clinicians, and therefore to improving patient care.
Book Synopsis Regression Modeling Strategies by : Frank E. Harrell
Download or read book Regression Modeling Strategies written by Frank E. Harrell and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 583 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. This text realistically deals with model uncertainty and its effects on inference to achieve "safe data mining".
Book Synopsis Applied Bayesian Modelling by : Peter Congdon
Download or read book Applied Bayesian Modelling written by Peter Congdon and published by Wiley. This book was released on 2003-04-18 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of Bayesian statistics has grown significantly in recent years, and will undoubtedly continue to do so. Applied Bayesian Modelling is the follow-up to the author’s best selling book, Bayesian Statistical Modelling, and focuses on the potential applications of Bayesian techniques in a wide range of important topics in the social and health sciences. The applications are illustrated through many real-life examples and software implementation in WINBUGS – a popular software package that offers a simplified and flexible approach to statistical modelling. The book gives detailed explanations for each example – explaining fully the choice of model for each particular problem. The book · Provides a broad and comprehensive account of applied Bayesian modelling. · Describes a variety of model assessment methods and the flexibility of Bayesian prior specifications. · Covers many application areas, including panel data models, structural equation and other multivariate structure models, spatial analysis, survival analysis and epidemiology. · Provides detailed worked examples in WINBUGS to illustrate the practical application of the techniques described. All WINBUGS programs are available from an ftp site. The book provides a good introduction to Bayesian modelling and data analysis for a wide range of people involved in applied statistical analysis, including researchers and students from statistics, and the health and social sciences. The wealth of examples makes this book an ideal reference for anyone involved in statistical modelling and analysis.
Book Synopsis Bayesian Statistics for the Social Sciences by : David Kaplan
Download or read book Bayesian Statistics for the Social Sciences written by David Kaplan and published by Guilford Publications. This book was released on 2014-07-23 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bridging the gap between traditional classical statistics and a Bayesian approach, David Kaplan provides readers with the concepts and practical skills they need to apply Bayesian methodologies to their data analysis problems. Part I addresses the elements of Bayesian inference, including exchangeability, likelihood, prior/posterior distributions, and the Bayesian central limit theorem. Part II covers Bayesian hypothesis testing, model building, and linear regression analysis, carefully explaining the differences between the Bayesian and frequentist approaches. Part III extends Bayesian statistics to multilevel modeling and modeling for continuous and categorical latent variables. Kaplan closes with a discussion of philosophical issues and argues for an "evidence-based" framework for the practice of Bayesian statistics. User-Friendly Features *Includes worked-through, substantive examples, using large-scale educational and social science databases, such as PISA (Program for International Student Assessment) and the LSAY (Longitudinal Study of American Youth). *Utilizes open-source R software programs available on CRAN (such as MCMCpack and rjags); readers do not have to master the R language and can easily adapt the example programs to fit individual needs. *Shows readers how to carefully warrant priors on the basis of empirical data. *Companion website features data and code for the book's examples, plus other resources.
Book Synopsis Bayesian Disease Mapping by : Andrew B. Lawson
Download or read book Bayesian Disease Mapping written by Andrew B. Lawson and published by Chapman and Hall/CRC. This book was released on 2008-08-05 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focusing on data commonly found in public health databases and clinical settings, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology provides an overview of the main areas of Bayesian hierarchical modeling and its application to the geographical analysis of disease. The book explores a range of topics in Bayesian inference and modeling, including Markov chain Monte Carlo methods, Gibbs sampling, the Metropolis–Hastings algorithm, goodness-of-fit measures, and residual diagnostics. It also focuses on special topics, such as cluster detection; space-time modeling; and multivariate, survival, and longitudinal analyses. The author explains how to apply these methods to disease mapping using numerous real-world data sets pertaining to cancer, asthma, epilepsy, foot and mouth disease, influenza, and other diseases. In the appendices, he shows how R and WinBUGS can be useful tools in data manipulation and simulation. Applying Bayesian methods to the modeling of georeferenced health data, Bayesian Disease Mapping proves that the application of these approaches to biostatistical problems can yield important insights into data.
Book Synopsis Markov Chain Monte Carlo in Practice by : W.R. Gilks
Download or read book Markov Chain Monte Carlo in Practice written by W.R. Gilks and published by CRC Press. This book was released on 1995-12-01 with total page 505 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a family study of breast cancer, epidemiologists in Southern California increase the power for detecting a gene-environment interaction. In Gambia, a study helps a vaccination program reduce the incidence of Hepatitis B carriage. Archaeologists in Austria place a Bronze Age site in its true temporal location on the calendar scale. And in France,
Book Synopsis Bayesian Disease Mapping by : Andrew B. Lawson
Download or read book Bayesian Disease Mapping written by Andrew B. Lawson and published by CRC Press. This book was released on 2008-08-05 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focusing on data commonly found in public health databases and clinical settings, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology provides an overview of the main areas of Bayesian hierarchical modeling and its application to the geographical analysis of disease. The book explores a range of topics in Bayesian inference and
