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Fundamentals of Clinical Data Science

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ISBN: 9783319997131 Year: Pages: 219 DOI: 10.1007/978-3-319-99713-1 Language: English
Publisher: Springer Nature
Subject: Medicine (General) --- Biology
Added to DOAB on : 2020-02-05 11:21:13
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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.

Evidence-Based Health Informatics - Promoting Safety and Efficiency through Scientific Methods and Ethical Policy

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Book Series: Studies in Health Technology and Informatics ISSN: 09269630 18798365 ISBN: 9781614996347 9781614996354 Year: Volume: 222 Pages: 388 DOI: 10.3233/978-1-61499-635-4-3 Language: English
Publisher: IOS Press
Subject: Medical technology
Added to DOAB on : 2017-02-09 15:21:12
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"Health IT is a major field of investment in support of healthcare delivery, but patients and professionals tend to have systems imposed upon them by organizational policy or as a result of even higher policy decision. And, while many health IT systems are efficient and welcomed by their users, and are essential to modern healthcare, this is not the case for all. Unfortunately, some systems cause user frustration and result in inefficiency in use, and a few are known to have inconvenienced patients or even caused harm, including the occasional death. This book seeks to answer the need for better understanding of the importance of robust evidence to support health IT and to optimize investment in it; to give insight into health IT evidence and evaluation as its primary source; and to promote health informatics as an underpinning science demonstrating the same ethical rigour and proof of net benefit as is expected of other applied health technologies. The book is divided into three parts: the context and importance of evidence-based health informatics; methodological considerations of health IT evaluation as the source of evidence; and ensuring the relevance and application of evidence. A number of cross cutting themes emerge in each of these sections.This book seeks to inform the reader on the wide range of knowledge available, and the appropriateness of its use according to the circumstances. It is aimed at a wide readership and will be of interest to health policymakers, clinicians, health informaticians, the academic health informatics community, members of patient and policy organisations, and members of the vendor industry."

Keywords

IT --- health --- healthcare --- policy --- policymakers --- health informatics

Secondary Analysis of Electronic Health Records

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ISBN: 9783319437408 9783319437422 Year: Pages: 427 DOI: 10.1007/978-3-319-43742-2 Language: English
Publisher: Springer Nature
Subject: Medicine (General) --- Pharmacy and materia medica --- Biotechnology --- Electrical and Nuclear Engineering --- Medical technology
Added to DOAB on : 2017-02-14 10:17:52
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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.

Handbook on Craniofacial Superimposition

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ISBN: 9783319111377 Year: Pages: 205 DOI: 10.1007/978-3-319-11137-7 Language: English
Publisher: Springer Nature
Subject: Social Sciences --- Medicine (General) --- Computer Science
Added to DOAB on : 2020-02-04 11:21:09

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This open access handbook presents a trustable craniofacial superimposition methodological framework. It includes detailed technical and practical overviews, and discussions about the latest tools and open problems, covering the educational, technical, ethical, and security aspects of this forensic identification technique. The book will be of particular interest to researchers and practitioners in forensic anthropology and forensic ID, and also researchers in computational intelligence. It is the final result of a European project, New Methodologies and Protocols of Forensic Identification by Craniofacial Superimposition (MEPROCS). The project collaborators who contributed to this handbook are: S. Damas, O. Ibáñez, M.I. Huete, T. Kahana, C. Wilkinson, E. Ferguson, C. Erolin, C. Cattaneo, P.T. Jayaprakash, R. Jankauskas, F. Cavalli, K. Imaizumi, R. Vicente, D. Navega, E. Cunha, A.H. Ross, E. Veselovskaya, A. Abramov, P. Lestón, F. Molinero, E. Ruiz, F. Navarro, J. Cardoso, F. Viegas, D. Humpire, R. Hardiman, J. Clement, A. Valsecchi, B.R. Campomanes-Alvarez, C. Campomanes-Alvarez, A.S. Çağdır, T. Briers, M. Steyn, M. Viniero, D.N. Vieira, and O. Cordón. ; Authors among leading researchers and practitioners worldwide Reports successful collaborative project results First comprehensive guide on this important technique Open Access

Clinical Text Mining: Secondary Use of Electronic Patient Records

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ISBN: 9783319785028 9783319785035 Year: Pages: 181 DOI: https://doi.org/10.1007/978-3-319-78503-5 Language: English
Publisher: Springer Nature Grant: Riksbankens Jubileumsfond; Stockholm University
Subject: Medical technology
Added to DOAB on : 2018-06-22 15:52:54
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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.

Exploring Resilience

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Book Series: SpringerBriefs in Applied Sciences and Technology ISBN: 9783030031893 Year: Pages: 128 DOI: 10.1007/978-3-030-03189-3 Language: English
Publisher: Springer Nature
Subject: Economics --- Sociology --- Medicine (General) --- Environmental Sciences --- Agriculture (General)
Added to DOAB on : 2020-02-04 11:21:12
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Resilience has become an important topic on the safety research agenda and in organizational practice. Most empirical work on resilience has been descriptive, identifying characteristics of work and organizing activity which allow organizations to cope with unexpected situations. Fewer studies have developed testable models and theories that can be used to support interventions aiming to increase resilience and improve safety. In addition, the absent integration of different system levels from individuals, teams, organizations, regulatory bodies, and policy level in theory and practice imply that mechanisms through which resilience is linked across complex systems are not yet well understood. Scientific efforts have been made to develop constructs and models that present relationships; however, these cannot be characterized as sufficient for theory building. There is a need for taking a broader look at resilience practices as a foundation for developing a theoretical framework that can help improve safety in complex systems. This book does not advocate for one definition or one field of research when talking about resilience; it does not assume that the use of resilience concepts is necessarily positive for safety. We encourage a broad approach, seeking inspiration across different scientific and practical domains for the purpose of further developing resilience at a theoretical and an operational level of relevance for different high-risk industries. The aim of the book is twofold: 1. To explore different approaches for operationalization of resilience across scientific disciplines and system levels. 2. To create a theoretical foundation for a resilience framework across scientific disciplines and system levels. By presenting chapters from leading international authors representing different research disciplines and practical fields we develop suggestions and inspiration for the research community and practitioners in high-risk industries. This book is Open Access under a CC-BY licence. ; Explores different approaches for operationalization of resilience across scientific disciplines and system levels Creates a theoretical foundation for a resilience framework across scientific disciplines and system levels Develops suggestions and inspiration for the research community and practitioners in high-risk industries Presents chapters from leading international authors representing different research disciplines and practical fields

Leveraging Data Science for Global Health

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ISBN: 9783030479947 Year: Pages: 475 DOI: 10.1007/978-3-030-47994-7 Language: English
Publisher: Springer Nature
Subject: Medicine (General) --- Computer Science --- Economics
Added to DOAB on : 2020-09-01 00:01:25
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This open access book explores ways to leverage information technology and machine learning to combat disease and promote health, especially in resource-constrained settings. It focuses on digital disease surveillance through the application of machine learning to non-traditional data sources. Developing countries are uniquely prone to large-scale emerging infectious disease outbreaks due to disruption of ecosystems, civil unrest, and poor healthcare infrastructure – and without comprehensive surveillance, delays in outbreak identification, resource deployment, and case management can be catastrophic. In combination with context-informed analytics, students will learn how non-traditional digital disease data sources – including news media, social media, Google Trends, and Google Street View – can fill critical knowledge gaps and help inform on-the-ground decision-making when formal surveillance systems are insufficient.

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