Clinical Data-Mining
Clinical Data-Mining

Author: Irwin Epstein

Publisher: Oxford University Press

Published: 2010

Total Pages: 241

ISBN-13: 019533552X

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Clinical Data-Mining (CDM) involves the conceptualization, extraction, analysis, and interpretation of available clinical data for practice knowledge-building, clinical decision-making and practitioner reflection. Depending upon the type of data mined, CDM can be qualitative or quantitative; it is generally retrospective, but may be meaningfully combined with original data collection.Any research method that relies on the contents of case records or information systems data inevitably has limitations, but with proper safeguards these can be minimized. Among CDM's strengths however, are that it is unobtrusive, inexpensive, presents little risk to research subjects, and is ethically compatible with practitioner value commitments. When conducted by practitioners, CDM yields conceptual as well as data-driven insight into their own practice- and program-generated questions.This pocket guide, from a seasoned practice-based researcher, covers all the basics of conducting practitioner-initiated CDM studies or CDM doctoral dissertations, drawing extensively on published CDM studies and completed CDM dissertations from multiple social work settings in the United States, Australia, Israel, Hong Kong and the United Kingdom. In addition, it describes consulting principles for researchers interested in forging collaborative university-agency CDM partnerships, making it a practical tool for novice practitioner-researchers and veteran academic-researchers alike.As such, this book is an exceptional guide both for professionals conducting practice-based research as well as for social work faculty seeking an evidence-informed approach to practice-research integration.

Clinical Data-Mining in Practice-Based Research
Clinical Data-Mining in Practice-Based Research

Author: Irwin Epstein

Publisher: Routledge

Published: 2001

Total Pages: 209

ISBN-13: 0789017083

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This groundbreaking book will show you how to use existing patient records to do original research so you can custom-tailor programs to fit the specific needs of your department. Clinical Data-Mining in Practice-Based Research draws from the experiences of members of the Mount Sinai Department of Social Work staff. By analyzing case data, these professionals were able to identify biopsychosocial factors that affected social-health outcomes, and therefore to assess, maintain, and improve the quality of social work services. The detailed discussions in this book will help you apply these techniques toward improving your own service.

Cases on Health Outcomes and Clinical Data Mining
Cases on Health Outcomes and Clinical Data Mining

Author: Patricia B. Cerrito

Publisher: IGI Global

Published: 2010

Total Pages: 0

ISBN-13: 9781615207237

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"Because so much data is now becoming readily available to investigate health outcomes, it is important to examine just how statistical models are used to do this. This book studies health outcomes research using data mining techniques"--Provided by publisher.

Data Mining in Clinical Medicine
Data Mining in Clinical Medicine

Author: Carlos Fernández Llatas

Publisher: Humana Press

Published: 2014-11-24

Total Pages: 0

ISBN-13: 9781493919840

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This volume complies a set of Data Mining techniques and new applications in real biomedical scenarios. Chapters focus on innovative data mining techniques, biomedical datasets and streams analysis, and real applications. Written in the highly successful Methods in Molecular Biology series format, chapters are thought to show to Medical Doctors and Engineers the new trends and techniques that are being applied to Clinical Medicine with the arrival of new Information and Communication technologies Authoritative and practical, Data Mining in Clinical Medicine seeks to aid scientists with new approaches and trends in the field.

Fundamentals of Clinical Data Science
Fundamentals of Clinical Data Science

Author: Pieter Kubben

Publisher: Springer

Published: 2018-12-21

Total Pages: 218

ISBN-13: 3319997130

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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.

Medical Data Mining and Knowledge Discovery
Medical Data Mining and Knowledge Discovery

Author: Krzysztof J. Cios

Publisher: Physica

Published: 2001-01-12

Total Pages: 528

ISBN-13:

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Modern medicine generates, almost daily, huge amounts of heterogeneous data. For example, medical data may contain SPECT images, signals like ECG, clinical information like temperature, cholesterol levels, etc., as well as the physician's interpretation. Those who deal with such data understand that there is a widening gap between data collection and data comprehension. Computerized techniques are needed to help humans address this problem. This volume is devoted to the relatively young and growing field of medical data mining and knowledge discovery. As more and more medical procedures employ imaging as a preferred diagnostic tool, there is a need to develop methods for efficient mining in databases of images. Other significant features are security and confidentiality concerns. Moreover, the physician's interpretation of images, signals, or other technical data, is written in unstructured English which is very difficult to mine. This book addresses all these specific features.

Big Data Management and the Internet of Things for Improved Health Systems
Big Data Management and the Internet of Things for Improved Health Systems

Author: Mishra, Brojo Kishore

Publisher: IGI Global

Published: 2018-01-19

Total Pages: 329

ISBN-13: 1522552235

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Because of the increased access to high-speed Internet and smart phones, many patients have started to use mobile applications to manage various health needs. These devices and mobile apps are now increasingly used and integrated with telemedicine and telehealth via the medical Internet of Things (IoT). Big Data Management and the Internet of Things for Improved Health Systems is a critical scholarly resource that examines the digital transformation of healthcare. Featuring coverage on a broad range of topics, such as brain computer interface, data reduction techniques, and risk factors, this book is geared towards academicians, practitioners, researchers, and students seeking research on health and well-being data.

Clinical Text Mining
Clinical Text Mining

Author: Hercules Dalianis

Publisher: Springer

Published: 2018-05-14

Total Pages: 192

ISBN-13: 3319785036

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This open access book describes the results of natural language processing and machine learning methods applied to clinical text from electronic patient records. It is divided into twelve chapters. Chapters 1-4 discuss the history and background of the original paper-based patient records, their purpose, and how they are written and structured. These initial chapters do not require any technical or medical background knowledge. The remaining eight chapters are more technical in nature and describe various medical classifications and terminologies such as ICD diagnosis codes, SNOMED CT, MeSH, UMLS, and ATC. Chapters 5-10 cover basic tools for natural language processing and information retrieval, and how to apply them to clinical text. The difference between rule-based and machine learning-based methods, as well as between supervised and unsupervised machine learning methods, are also explained. Next, ethical concerns regarding the use of sensitive patient records for research purposes are discussed, including methods for de-identifying electronic patient records and safely storing patient records. The book’s closing chapters present a number of applications in clinical text mining and summarise the lessons learned from the previous chapters. The book provides a comprehensive overview of technical issues arising in clinical text mining, and offers a valuable guide for advanced students in health informatics, computational linguistics, and information retrieval, and for researchers entering these fields.

Diagnostic Applications of Health Intelligence and Surveillance Systems
Diagnostic Applications of Health Intelligence and Surveillance Systems

Author: Yadav, Divakar

Publisher: IGI Global

Published: 2021-01-15

Total Pages: 332

ISBN-13: 1799865282

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Health surveillance and intelligence play an important role in modern health systems as more data must be collected and analyzed. It is crucial that this data is interpreted and analyzed effectively and efficiently in order to assist with diagnoses and predictions. Diagnostic Applications of Health Intelligence and Surveillance Systems is an essential reference book that examines recent studies that are driving artificial intelligence in the health sector and helping practitioners to predict and diagnose diseases. Chapters within the book focus on health intelligence and how health surveillance data can be made useful and meaningful. Covering topics that include computational intelligence, data analytics, mobile health, and neural networks, this book is crucial for healthcare practitioners, IT specialists, academicians, researchers, and students.

Healthcare Data Analytics
Healthcare Data Analytics

Author: Chandan K. Reddy

Publisher: CRC Press

Published: 2015-06-23

Total Pages: 756

ISBN-13: 148223212X

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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