Clinical Data Mining

Clinical Data Mining
Author: Irwin Epstein
Publsiher: Oxford University Press
Total Pages: 241
Release: 2010
Genre: Social Science
ISBN: 9780195335521

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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,Susan Blumenfield
Publsiher: Routledge
Total Pages: 209
Release: 2001
Genre: Business & Economics
ISBN: 9780789017086

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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
Publsiher: IGI Global
Total Pages: 0
Release: 2010
Genre: Computers
ISBN: 1615207236

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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 and Medical Knowledge Management Cases and Applications

Data Mining and Medical Knowledge Management  Cases and Applications
Author: Berka, Petr,Rauch, Jan,Zighed, Djamel Abdelkader
Publsiher: IGI Global
Total Pages: 464
Release: 2009-02-28
Genre: Computers
ISBN: 9781605662190

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The healthcare industry produces a constant flow of data, creating a need for deep analysis of databases through data mining tools and techniques resulting in expanded medical research, diagnosis, and treatment. Data Mining and Medical Knowledge Management: Cases and Applications presents case studies on applications of various modern data mining methods in several important areas of medicine, covering classical data mining methods, elaborated approaches related to mining in electroencephalogram and electrocardiogram data, and methods related to mining in genetic data. A premier resource for those involved in data mining and medical knowledge management, this book tackles ethical issues related to cost-sensitive learning in medicine and produces theoretical contributions concerning general problems of data, information, knowledge, and ontologies.

Data Mining in Clinical Medicine

Data Mining in Clinical Medicine
Author: Carlos Fernández Llatas,Juan Miguel García-Gómez
Publsiher: Humana Press
Total Pages: 0
Release: 2014-11-24
Genre: Science
ISBN: 1493919849

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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.

Medical Data Mining and Knowledge Discovery

Medical Data Mining and Knowledge Discovery
Author: Krzysztof J. Cios
Publsiher: Physica
Total Pages: 528
Release: 2001-01-12
Genre: Computers
ISBN: UOM:39015051314717

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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.

Artificial Intelligence and Data Mining in Healthcare

Artificial Intelligence and Data Mining in Healthcare
Author: Malek Masmoudi,Bassem Jarboui,Patrick Siarry
Publsiher: Springer Nature
Total Pages: 211
Release: 2021-01-25
Genre: Computers
ISBN: 9783030452407

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This book presents recent work on healthcare management and engineering using artificial intelligence and data mining techniques. Specific topics covered in the contributed chapters include predictive mining, decision support, capacity management, patient flow optimization, image compression, data clustering, and feature selection. The content will be valuable for researchers and postgraduate students in computer science, information technology, industrial engineering, and applied mathematics.

Clinical Text Mining

Clinical Text Mining
Author: Hercules Dalianis
Publsiher: Springer
Total Pages: 192
Release: 2018-05-14
Genre: Computers
ISBN: 9783319785035

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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.