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Semistructured black-box prediction: proposed approach for asthma admissions in London

Authors Soyiri I, Reidpath D

Received 9 June 2012

Accepted for publication 16 July 2012

Published 20 August 2012 Volume 2012:5 Pages 693—705


Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 3

Ireneous N Soyiri,1,2 Daniel D Reidpath1

1Global Public Health, School of Medicine and Health Sciences, Monash University, Malaysia; 2School of Public Health, University of Ghana, Accra, Ghana

Abstract: Asthma is a global public health problem and the most common chronic disease among children. The factors associated with the condition are diverse, and environmental factors appear to be the leading cause of asthma exacerbation and its worsening disease burden. However, it remains unknown how changes in the environment affect asthma over time, and how temporal or environmental factors predict asthma events. The methodologies for forecasting asthma and other similar chronic conditions are not comprehensively documented anywhere to account for semistructured noncausal forecasting approaches. This paper highlights and discusses practical issues associated with asthma and the environment, and suggests possible approaches for developing decision-making tools in the form of semistructured black-box models, which is relatively new for asthma. Two statistical methods which can potentially be used in predictive modeling and health forecasting for both anticipated and peak events are suggested. Importantly, this paper attempts to bridge the areas of epidemiology, environmental medicine and exposure risks, and health services provision. The ideas discussed herein will support the development and implementation of early warning systems for chronic respiratory conditions in large populations, and ultimately lead to better decision-making tools for improving health service delivery.

Keywords: asthma, health care, black-box forecast, chronic, epidemiology, environment, respiratory risk

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