國立高雄大學圖資館 |

Language: English

Back

Causation in population health infor...

Dammann, Olaf.

Causation in population health informatics and data science

Record Type:	Electronic resources : Monograph/item
Title/Author:	Causation in population health informatics and data scienceby Olaf Dammann, Benjamin Smart.
Author:	Dammann, Olaf.
other author:	Smart, Benjamin.
Published:	Cham :Springer International Publishing :2019.
Description:	ix, 134 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	EpidemiologyData processing.
Online resource:	https://doi.org/10.1007/978-3-319-96307-5
ISBN:	9783319963075$q(electronic bk.)

Causation in population health informatics and data science
Dammann, Olaf.

Causation in population health informatics and data science[electronic resource] /by Olaf Dammann, Benjamin Smart. - Cham :Springer International Publishing :2019. - ix, 134 p. :ill. (some col.), digital ;24 cm.

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.

ISBN: 9783319963075$q(electronic bk.)

Standard No.: 10.1007/978-3-319-96307-5doiSubjects--Topical Terms:

224410
Epidemiology
--Data processing.

LC Class. No.: RA652.2.D38 / D366 2019

Dewey Class. No.: 614.4

Causation in population health informatics and data science
LDR:02017nmm a2200337 a 4500 001 553351
003 DE-He213
005 20190604112034.0
006 m d
007 cr nn 008maaau
008 191111s2019 gw s 0 eng d
020 $a 9783319963075$q(electronic bk.)
020 $a 9783319963068$q(paper)
024 7 $a 10.1007/978-3-319-96307-5 $2 doi
035 $a 978-3-319-96307-5
040 $a GP $c GP
041 0 $a eng
050 4 $a RA652.2.D38 $b D366 2019
072 7 $a MBG $2 bicssc
072 7 $a MED000000 $2 bisacsh
072 7 $a MBG $2 thema
072 7 $a UB $2 thema
082 0 4 $a 614.4 $2 23
090 $a RA652.2.D38 $b D162 2019
100 1 $a Dammann, Olaf. $3 834560
245 1 0 $a Causation in population health informatics and data science $h [electronic resource] / $c by Olaf Dammann, Benjamin Smart.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a ix, 134 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a Introduction -- Data Interpretation -- Data Generation -- Informatics -- Philosophy -- Causal inference -- Knowledge Integration -- Systems Thinking -- Summary and conclusion.
520 $a 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.
650 0 $a Epidemiology $x Data processing. $3 224410
650 0 $a Medical informatics. $3 196487
650 0 $a Data mining. $3 184440
650 1 4 $a Health Informatics. $3 274212
650 2 4 $a Logic. $3 180785
650 2 4 $a Epidemiology. $3 191577
700 1 $a Smart, Benjamin. $3 827407
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
856 4 0 $u https://doi.org/10.1007/978-3-319-96307-5
950 $a Medicine (Springer-11650)