IMPACT: A generalisable system for simulating public health interventions

Iain Buchan; John Ainsworth; Emma Carruthers; Philip Couch; Martin O'Flaherty; Duncan Smith; Richard Williams; Simon Capewell

doi:10.3233/978-1-60750-588-4-486

IMPACT: A generalisable system for simulating public health interventions

Iain Buchan, John Ainsworth, Emma Carruthers, Philip Couch, Martin O'Flaherty, Duncan Smith, Richard Williams, Simon Capewell

Cancer Research UK Manchester Institute

Research output: Contribution to journal › Article › peer-review

Abstract

Populations are under-served by local health policies and management of resources, partly because of a lack of realistically complex models to enable a wide range of potential options to be appraised. Rising computing power coupled with advances in machine learning and healthcare information now enables such models to be constructed and executed. However, such models are not generally accessible to public health practitioners because they do not have the requisite technical knowledge or skills. This paper presents a system for creating, executing and analyzing the results of simulated public health and healthcare policy interventions, which is more accessible and usable by modellers and policy-makers alike.

Original language	English
Pages (from-to)	486-90
Number of pages	5
Journal	Studies in Health Technology and Informatics
Volume	160
Issue number	Pt 1
DOIs	https://doi.org/10.3233/978-1-60750-588-4-486
Publication status	Published - 2010

Keywords

Computer Simulation
Coronary Artery Disease
Great Britain
Humans
Models, Theoretical
Prevalence
Proportional Hazards Models
Public Health Informatics
Survival Analysis
Survival Rate
Journal Article
Research Support, Non-U.S. Gov't

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10.3233/978-1-60750-588-4-486

http://www.ncbi.nlm.nih.gov/pubmed/20841734

Cite this

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abstract = "Populations are under-served by local health policies and management of resources, partly because of a lack of realistically complex models to enable a wide range of potential options to be appraised. Rising computing power coupled with advances in machine learning and healthcare information now enables such models to be constructed and executed. However, such models are not generally accessible to public health practitioners because they do not have the requisite technical knowledge or skills. This paper presents a system for creating, executing and analyzing the results of simulated public health and healthcare policy interventions, which is more accessible and usable by modellers and policy-makers alike.",

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T2 - A generalisable system for simulating public health interventions

AU - Buchan, Iain

AU - Ainsworth, John

AU - Carruthers, Emma

AU - Couch, Philip

AU - O'Flaherty, Martin

AU - Smith, Duncan

AU - Williams, Richard

AU - Capewell, Simon

PY - 2010

Y1 - 2010

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AB - Populations are under-served by local health policies and management of resources, partly because of a lack of realistically complex models to enable a wide range of potential options to be appraised. Rising computing power coupled with advances in machine learning and healthcare information now enables such models to be constructed and executed. However, such models are not generally accessible to public health practitioners because they do not have the requisite technical knowledge or skills. This paper presents a system for creating, executing and analyzing the results of simulated public health and healthcare policy interventions, which is more accessible and usable by modellers and policy-makers alike.

KW - Computer Simulation

KW - Coronary Artery Disease

KW - Great Britain

KW - Humans

KW - Models, Theoretical

KW - Prevalence

KW - Proportional Hazards Models

KW - Public Health Informatics

KW - Survival Analysis

KW - Survival Rate

KW - Journal Article

KW - Research Support, Non-U.S. Gov't

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DO - 10.3233/978-1-60750-588-4-486

M3 - Article

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SN - 0926-9630

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EP - 490

JO - Studies in Health Technology and Informatics

JF - Studies in Health Technology and Informatics

IS - Pt 1

ER -

IMPACT: A generalisable system for simulating public health interventions

Abstract

Keywords

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