Causation in Population Health Informatics and Data Science

Dettagli prodotto

Casa editrice

Springer International Publishing

Pubblicato

2019

Lingua

English

Pagine

144

Info

144 Pagine
235 mm x 155 mm

ISBN

978-3-030-07174-5

Hauptbeschreibung

Marketing text: This book covers the overlap between informatics, computer science, philosophy of causation, and causal inference in epidemiology and population health research. Key concepts covered include how data are generated and interpreted, and how and why concepts in health informatics and the philosophy of science should be integrated in a systems-thinking approach. Furthermore, a formal epistemology for the health sciences and public health is suggested.

Causation in Population Health Informatics and Data Science 
provides a detailed guide of the latest thinking on causal inference in population health informatics. It is therefore a critical resource for all informaticians and epidemiologists interested in the potential benefits of utilising a systems-based approach to causal inference in health informatics.

Inhaltsverzeichnis

Introduction.- Data Interpretation.- Data Generation.- Informatics.- Philosophy.- Causal inference.- Knowledge Integration.- Systems Thinking.- Summary and conclusion.

Klappentext

Marketing text: This book covers the overlap between informatics, computer science, philosophy of causation, and causal inference in epidemiology and population health research. Key concepts covered include how data are generated and interpreted, and how and why concepts in health informatics and the philosophy of science should be integrated in a systems-thinking approach. Furthermore, a formal epistemology for the health sciences and public health is suggested.

Causation in Population Health Informatics and Data Science 
provides a detailed guide of the latest thinking on causal inference in population health informatics. It is therefore a critical resource for all informaticians and epidemiologists interested in the potential benefits of utilising a systems-based approach to causal inference in health informatics.

This will be the first interdisciplinary book ever to attempt an integration of epidemiology/public health, computation/informatics, and philosophy of science for causal inference


The book will offer both a review of current research at the three intersections between the three fields and a new theory of how all three can be integrated to improve public health research


The book will not provide an historical overview, but it will zoom in on causal inference in public health and how informatics/computational approaches can help identify causal associations in the health sciences

Informazioni sull'autore

Olaf Dammann, M.D. (U Hamburg, ’90), S.M. Epidemiology (Harvard, ’97) is Professor of Public Health and Community Medicine, Pediatrics, and Ophthalmology at Tufts University School of Medicine in Boston, USA. He is also Editor-in-Chief Emeritus of PEDIATRIC RESEARCH, the publication of the International Pediatric Research Foundation. His research interests include the elucidation of risk factors for brain damage and retinopathy in preterm newborns, the theory of risk and causation in biomedical and population health research, and the development of computational chronic disease models. He has received grant support from the National Institutes of Health and the European Union. His bibliography lists more than 200 publications.Dr Benjamin Smart is senior lecturer at The University of Johannesburg, and a founder member of The African Centre for Epistemology and Philosophy of Science. He was awarded a PhD in metaphysics by The University of Nottingham in 2012, before lecturing philosophy at The University of Birmingham (2012-2015). Smart has published widely on causation, laws of nature, and on the philosophy of health and disease.