東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Causation in population health infor...

Dammann, Olaf.

Linked to FindBook

Google Book

Amazon

博客來

Causation in population health informatics and data science

Record Type:	Electronic resources : Monograph/item
Title/Author:	Causation in population health informatics and data science/ by 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.
[NT 15003449]:	Introduction -- Data Interpretation -- Data Generation -- Informatics -- Philosophy -- Causal inference -- Knowledge Integration -- Systems Thinking -- Summary and conclusion.
Contained By:	Springer eBooks
Subject:	Epidemiology - Data processing. -
Online resource:	https://doi.org/10.1007/978-3-319-96307-5
ISBN:	9783319963075

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

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

3381052
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 2177690
003 DE-He213
005 20190604112034.0
006 m d
007 cr nn 008maaau
008 191122s2019 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 3381050
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 3381052
650 0 $a Medical informatics. $3 661258
650 0 $a Data mining. $3 562972
650 1 4 $a Health Informatics. $3 892928
650 2 4 $a Logic. $3 529544
650 2 4 $a Epidemiology. $3 568544
700 1 $a Smart, Benjamin. $3 3381051
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u https://doi.org/10.1007/978-3-319-96307-5
950 $a Medicine (Springer-11650)