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Author: Chandan K. Reddy
Publisher: CRC Press
ISBN: 148223212X
Category : Business & Economics
Languages : en
Pages : 756
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Book Description
At the intersection of computer science and healthcare, data analytics has emerged as a promising tool for solving problems across many healthcare-related disciplines. Supplying a comprehensive overview of recent healthcare analytics research, Healthcare Data Analytics provides a clear understanding of the analytical techniques currently available
Author: Chandan K. Reddy
Publisher: CRC Press
ISBN: 148223212X
Category : Business & Economics
Languages : en
Pages : 756
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Book Description
At the intersection of computer science and healthcare, data analytics has emerged as a promising tool for solving problems across many healthcare-related disciplines. Supplying a comprehensive overview of recent healthcare analytics research, Healthcare Data Analytics provides a clear understanding of the analytical techniques currently available
Author: Jennifer Peat
Publisher: John Wiley & Sons
ISBN: 0470755202
Category : Medical
Languages : en
Pages : 336
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Book Description
Holistic approach to understanding medical statistics This hands-on guide is much more than a basic medical statistics introduction. It equips you with the statistical tools required for evidence-based clinical research. Each chapter provides a clear step-by-step guide to each statistical test with practical instructions on how to generate and interpret the numbers, and present the results as scientific tables or graphs. Showing you how to: analyse data with the help of data set examples (Click here to download datasets) select the correct statistics and report results for publication or presentation understand and critically appraise results reported in the literature Each statistical test is linked to the research question and the type of study design used. There are also checklists for critically appraising the literature and web links to useful internet sites. Clear and concise explanations, combined with plenty of examples and tabulated explanations are based on the authors’ popular medical statistics courses. Critical appraisal guidelines at the end of each chapter help the reader evaluate the statistical data in their particular contexts.
Author: Christo El Morr
Publisher: Springer
ISBN: 3030045064
Category : Medical
Languages : en
Pages : 113
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Book Description
This book offers a practical introduction to healthcare analytics that does not require a background in data science or statistics. It presents the basics of data, analytics and tools and includes multiple examples of their applications in the field. The book also identifies practical challenges that fuel the need for analytics in healthcare as well as the solutions to address these problems. In the healthcare field, professionals have access to vast amount of data in the form of staff records, electronic patient record, clinical findings, diagnosis, prescription drug, medical imaging procedure, mobile health, resources available, etc. Managing the data and analyzing it to properly understand it and use it to make well-informed decisions can be a challenge for managers and health care professionals. A new generation of applications, sometimes referred to as end-user analytics or self-serve analytics, are specifically designed for non-technical users such as managers and business professionals. The ability to use these increasingly accessible tools with the abundant data requires a basic understanding of the core concepts of data, analytics, and interpretation of outcomes. This book is a resource for such individuals to demystify and learn the basics of data management and analytics for healthcare, while also looking towards future directions in the field.
Author: Pieter Kubben
Publisher: Springer
ISBN: 3319997130
Category : Medical
Languages : en
Pages : 218
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Book Description
This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.
Author: Kun Chang Lee
Publisher: Academic Press
ISBN: 0128193158
Category : Science
Languages : en
Pages : 298
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Book Description
Data Analytics in Biomedical Engineering and Healthcare explores key applications using data analytics, machine learning, and deep learning in health sciences and biomedical data. The book is useful for those working with big data analytics in biomedical research, medical industries, and medical research scientists. The book covers health analytics, data science, and machine and deep learning applications for biomedical data, covering areas such as predictive health analysis, electronic health records, medical image analysis, computational drug discovery, and genome structure prediction using predictive modeling. Case studies demonstrate big data applications in healthcare using the MapReduce and Hadoop frameworks. - Examines the development and application of data analytics applications in biomedical data - Presents innovative classification and regression models for predicting various diseases - Discusses genome structure prediction using predictive modeling - Shows readers how to develop clinical decision support systems - Shows researchers and specialists how to use hybrid learning for better medical diagnosis, including case studies of healthcare applications using the MapReduce and Hadoop frameworks
Author: Geoff Der
Publisher: CRC Press
ISBN: 9781584884699
Category : Mathematics
Languages : en
Pages : 450
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Book Description
Statistical analysis is ubiquitous in modern medical research. Logistic regression, generalized linear models, random effects models, and Cox's regression all have become commonplace in the medical literature. But while statistical software such as SAS make routine application of these techniques possible, users who are not primarily statisticians must take care to correctly implement the various procedures and correctly interpret the output. Statistical Analysis of Medical Data Using SAS demonstrates how to use SAS to analyze medical data. Each chapter addresses a particular analysis method. The authors briefly describe each procedure, but focus on its SAS implementation and properly interpreting the output. The carefully designed presentation relegates the theoretical details to "Displays," so that the code and results can be explored without interruption. All of the code and data sets used in the book are available for download from either the SAS Web site or www.crcpress.com. Der and Everitt, authors of the best-selling Handbook of Statistical Analyses Using SAS, bring all of their considerable talent and experience to bear in this book. Step-by-step instructions, lucid explanations and clear examples combine to form an outstanding, self-contained guide--suitable for medical researchers and statisticians alike--to using SAS to analyze medical data.
Author: Don Hong
Publisher: World Scientific
ISBN: 9814476234
Category : Medical
Languages : en
Pages : 364
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Book Description
Quantitative biomedical data analysis is a fast-growing interdisciplinary area of applied and computational mathematics, statistics, computer science, and biomedical science, leading to new fields such as bioinformatics, biomathematics, and biostatistics. In addition to traditional statistical techniques and mathematical models using differential equations, new developments with a very broad spectrum of applications, such as wavelets, spline functions, curve and surface subdivisions, sampling, and learning theory, have found their mathematical home in biomedical data analysis.This book gives a new and integrated introduction to quantitative medical data analysis from the viewpoint of biomathematicians, biostatisticians, and bioinformaticians. It offers a definitive resource to bridge the disciplines of mathematics, statistics, and biomedical sciences. Topics include mathematical models for cancer invasion and clinical sciences, data mining techniques and subset selection in data analysis, survival data analysis and survival models for cancer patients, statistical analysis and neural network techniques for genomic and proteomic data analysis, wavelet and spline applications for mass spectrometry data preprocessing and statistical computing.
Author: Petra Perner
Publisher: Springer Science & Business Media
ISBN: 354020282X
Category : Medical
Languages : en
Pages : 135
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Book Description
This book constitutes the refereed proceedings of the 4th International Symposium on Medical Data Analysis, ISMDA 2003, held in Berlin, Germany in October 2003. The 15 revised full papers presented were carefully reviewed and selected for inclusion in the book. The papers are organized in topical sections on medical models and learning, integration of intelligent analysis methods into medical databases, medical signal processing and image analysis, and applications of medical diagnostic support systems.
Author: Ton J. Cleophas
Publisher: Springer
ISBN: 3319395866
Category : Medical
Languages : en
Pages : 241
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Book Description
This textbook consists of ten chapters, and is a must-read to all medical and health professionals, who already have basic knowledge of how to analyze their clinical data, but still, wonder, after having done so, why procedures were performed the way they were. The book is also a must-read to those who tend to submerge in the flood of novel statistical methodologies, as communicated in current clinical reports, and scientific meetings. In the past few years, the HOW-SO of current statistical tests has been made much more simple than it was in the past, thanks to the abundance of statistical software programs of an excellent quality. However, the WHY-SO may have been somewhat under-emphasized. For example, why do statistical tests constantly use unfamiliar terms, like probability distributions, hypothesis testing, randomness, normality, scientific rigor, and why are Gaussian curves so hard, and do they make non-mathematicians getting lost all the time? The book will cover the WHY-SOs.
Author: MIT Critical Data
Publisher: Springer
ISBN: 3319437429
Category : Medical
Languages : en
Pages : 435
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Book Description
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.
