Predicting paediatric asthma hospital admissions and ED visits

Abstract

Historical data for hospital admissions and Emergency Department (ED) visits in Baltimore City contain information concerning temporal patterns of paediatric asthma service utilisation (e.g. number of peaks and troughs, timing, relative magnitudes, steepness of rise and fall of the endemic cycles, etc.). This historical information can be captured by linear and neural network models to accurately predict the level of asthma admissions for the next few days or one week. Using 14 years of data, the best neural network models explained over 80% of the variations in admissions data with root mean square errors of 5–7 admissions per week. Models developed to predict asthma admissions can aid in identifying future peak periods of asthma admissions, alerting and educating individual asthmatic patients to periods of increased risk, and mitigating asthma events that lead to ED and/or hospital admissions. It is believed that these modelling techniques using historical data can be applied to any city or region with similar accuracies.