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Predicting pediatric asthma admissions for Barbados
J ALLERGY CLIN IMMUNOL VOLUME 113, NUMBER 2
Abstracts S305
information can be used to aid in identifying future peak periods of asthma admissions, alert health officials and educate individual patients of increased risk and mitigate asthma events that lead to A&ED admissions. Funding: University of the West Indies, NASA
Predicting Pediatric Asthma Admissions for Barbados
E. Blades1, D. Kimes2, E. Levine2, G. Mathison1, H. Thani3, M. Lavoie1; 1Biological and Chemical Sciences, University of the West Indies, Bridgetown, BARBADOS, 2Goddard Space Flight Center, NASA, Maryland, WA, 3University of the West Indies, Queen Elizabeth Hospital, Bridgetown, BARBADOS. RATIONALE: To predict future admissions of pediatric asthma using temporal patterns embedded in 6 years of historical data. METHODS: Data from 1997 to 2002 on asthmatic admissions to the Accident and Emergency Department (A&ED) of the Queen Elizabeth Hospital, Barbados, were analyzed using neural network models. Weekly admissions were modeled using the previous ten weeks of admissions and knowledge of the week being predicted (1-52). Three hundred and two data sets were produced for modeling (52 weeks x 6 years) minus 10 lead weeks. RESULTS: Pediatric admissions to A&ED for asthma over the study period averaged 63% of total asthmatics attending. 1997 had the highest number of admissions and 2001 the lowest. The major peak in admissions for all years occurs between weeks 34 to 48 (mid September to end of November). The best neural network model had a high prediction accuracy, with root mean square errors of 22 admissions per week (average admissions per week were 122) and an R2 of 0.78. CONCLUSION: Models developed to predict asthma admissions can accurately predict the level of asthma admissions for the next week. This
TUESDAY
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Abstracts S305
information can be used to aid in identifying future peak periods of asthma admissions, alert health officials and educate individual patients of increased risk and mitigate asthma events that lead to A&ED admissions. Funding: University of the West Indies, NASA
Predicting Pediatric Asthma Admissions for Barbados
E. Blades1, D. Kimes2, E. Levine2, G. Mathison1, H. Thani3, M. Lavoie1; 1Biological and Chemical Sciences, University of the West Indies, Bridgetown, BARBADOS, 2Goddard Space Flight Center, NASA, Maryland, WA, 3University of the West Indies, Queen Elizabeth Hospital, Bridgetown, BARBADOS. RATIONALE: To predict future admissions of pediatric asthma using temporal patterns embedded in 6 years of historical data. METHODS: Data from 1997 to 2002 on asthmatic admissions to the Accident and Emergency Department (A&ED) of the Queen Elizabeth Hospital, Barbados, were analyzed using neural network models. Weekly admissions were modeled using the previous ten weeks of admissions and knowledge of the week being predicted (1-52). Three hundred and two data sets were produced for modeling (52 weeks x 6 years) minus 10 lead weeks. RESULTS: Pediatric admissions to A&ED for asthma over the study period averaged 63% of total asthmatics attending. 1997 had the highest number of admissions and 2001 the lowest. The major peak in admissions for all years occurs between weeks 34 to 48 (mid September to end of November). The best neural network model had a high prediction accuracy, with root mean square errors of 22 admissions per week (average admissions per week were 122) and an R2 of 0.78. CONCLUSION: Models developed to predict asthma admissions can accurately predict the level of asthma admissions for the next week. This
TUESDAY
1118