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

Reidpath, Daniel D.; Soyiri, Ireneous N.

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. © 2012 Soyiri and Reidpath, publisher and licensee Dove Medical Press Ltd.

Citation

Reidpath, D. D., & Soyiri, I. N. (2012). Semistructured black-box prediction: proposed approach for asthma admissions in London. International Journal of General Medicine, 5, 693-705. https://doi.org/10.2147/IJGM.S34647

• Journal Article Type: Article
• Acceptance Date: Jul 16, 2012
• Online Publication Date: Aug 20, 2012
• Publication Date: 2012
• Deposit Date: May 15, 2019
• Publicly Available Date: May 21, 2019
• Journal: International Journal of General Medicine
• Print ISSN: 1178-7074
• Electronic ISSN: 1178-7074
• Publisher: Dove Press
• Peer Reviewed: Peer Reviewed
• Volume: 5
• Pages: 693-705
• DOI: https://doi.org/10.2147/IJGM.S34647
• Keywords: General Medicine
• Public URL: https://hull-repository.worktribe.com/output/1755803