Causation in Population Health Informatics and Data Science
Dammann, Olaf.
Causation in Population Health Informatics and Data Science by Olaf Dammann, Benjamin Smart. - IX, 134 páginas 15 ilustraciones, 1 ilustraciones a color - Medicine (Springer-11650) .
Introduction -- Data Interpretation -- Data Generation -- Informatics -- Philosophy -- Causal inference -- Knowledge Integration -- Systems Thinking -- Summary and conclusion.
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.
9783319963075
10.1007/978-3-319-96307-5 doi
Medical records Logic. Epidemiology.
RA652.2 / .D38D36 2019EB
Causation in Population Health Informatics and Data Science by Olaf Dammann, Benjamin Smart. - IX, 134 páginas 15 ilustraciones, 1 ilustraciones a color - Medicine (Springer-11650) .
Introduction -- Data Interpretation -- Data Generation -- Informatics -- Philosophy -- Causal inference -- Knowledge Integration -- Systems Thinking -- Summary and conclusion.
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.
9783319963075
10.1007/978-3-319-96307-5 doi
Medical records Logic. Epidemiology.
RA652.2 / .D38D36 2019EB