Causation in Population Health Informatics and Data Science

ISBN-13: 9783030071745 / Angielski / Miękka / 2019 / 134 str.

Olaf Dammann; Benjamin Smart

Spis treści

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

O autorze

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.

Blurb

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.