Scenario analysis for local area life expectancy using conditional independence maps

I. H. Jarman; T. A. Etchells; C. Perkins; M. A. Bellis; P. J.G. Lisboa

doi:10.1109/DeSE.2011.110

Scenario analysis for local area life expectancy using conditional independence maps

I. H. Jarman^*, T. A. Etchells, C. Perkins, M. A. Bellis, P. J.G. Lisboa

^*Corresponding author for this work

Research output: Contribution to conference › Paper › peer-review

1 Citation (Scopus)

Abstract

The challenge of sustainable healthcare requires significant changes to the structure of healthcare delivery, with greater emphasis on prevention and pro-active, personalised care. This, in turn, relies for its effectiveness on detailed analysis of the evidence contained in large data bases. This paper describes a powerful and general analytical approach to derive insights from complex health and behavioural data, and to underpin evidence-based scenario analysis for commissioning of clinically efficient and cost effective health interventions.

Original language	English
Pages	92-97
Number of pages	6
DOIs	https://doi.org/10.1109/DeSE.2011.110
Publication status	Published - 2011
Externally published	Yes
Event	4th International Conference on Developments in eSystems Engineering, DeSE 2011 - Dubai, United Arab Emirates Duration: 6 Dec 2011 → 8 Dec 2011

Conference

Conference	4th International Conference on Developments in eSystems Engineering, DeSE 2011
Country/Territory	United Arab Emirates
City	Dubai
Period	6/12/11 → 8/12/11

Keywords

Bayesian networks
Conditional independence
Mutual information
Scenario analysis

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/DeSE.2011.110

Cite this

@conference{a9d001ff54bf4cd9ae226ff3e481a794,

title = "Scenario analysis for local area life expectancy using conditional independence maps",

abstract = "The challenge of sustainable healthcare requires significant changes to the structure of healthcare delivery, with greater emphasis on prevention and pro-active, personalised care. This, in turn, relies for its effectiveness on detailed analysis of the evidence contained in large data bases. This paper describes a powerful and general analytical approach to derive insights from complex health and behavioural data, and to underpin evidence-based scenario analysis for commissioning of clinically efficient and cost effective health interventions.",

keywords = "Bayesian networks, Conditional independence, Mutual information, Scenario analysis",

author = "Jarman, {I. H.} and Etchells, {T. A.} and C. Perkins and Bellis, {M. A.} and Lisboa, {P. J.G.}",

year = "2011",

doi = "10.1109/DeSE.2011.110",

language = "English",

pages = "92--97",

note = "4th International Conference on Developments in eSystems Engineering, DeSE 2011 ; Conference date: 06-12-2011 Through 08-12-2011",

}

TY - CONF

T1 - Scenario analysis for local area life expectancy using conditional independence maps

AU - Jarman, I. H.

AU - Etchells, T. A.

AU - Perkins, C.

AU - Bellis, M. A.

AU - Lisboa, P. J.G.

PY - 2011

Y1 - 2011

N2 - The challenge of sustainable healthcare requires significant changes to the structure of healthcare delivery, with greater emphasis on prevention and pro-active, personalised care. This, in turn, relies for its effectiveness on detailed analysis of the evidence contained in large data bases. This paper describes a powerful and general analytical approach to derive insights from complex health and behavioural data, and to underpin evidence-based scenario analysis for commissioning of clinically efficient and cost effective health interventions.

AB - The challenge of sustainable healthcare requires significant changes to the structure of healthcare delivery, with greater emphasis on prevention and pro-active, personalised care. This, in turn, relies for its effectiveness on detailed analysis of the evidence contained in large data bases. This paper describes a powerful and general analytical approach to derive insights from complex health and behavioural data, and to underpin evidence-based scenario analysis for commissioning of clinically efficient and cost effective health interventions.

KW - Bayesian networks

KW - Conditional independence

KW - Mutual information

KW - Scenario analysis

UR - http://www.scopus.com/inward/record.url?scp=84858057440&partnerID=8YFLogxK

U2 - 10.1109/DeSE.2011.110

DO - 10.1109/DeSE.2011.110

M3 - Paper

AN - SCOPUS:84858057440

SP - 92

EP - 97

T2 - 4th International Conference on Developments in eSystems Engineering, DeSE 2011

Y2 - 6 December 2011 through 8 December 2011

ER -

Scenario analysis for local area life expectancy using conditional independence maps

Abstract

Conference

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this