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Using CART for advanced prediction of asthma attacks based on telemonitoring data

Advanced prediction of asthma exacerbations may significantly improve patient quality of life and reduce costs of urgent care delivery. Majority of current algorithms predict who is likely to experience asthma exacerbation rather than when it is about to occur. We used data from asthma home-based telemonitoring for advanced prediction of asthma exacerbation. The goal of this project was to develop an algorithm that predicts asthma exacerbation one day in advance based on previous 7-day window. CART was used for predictive modeling. Resulting algorithm had specificity 0.971, sensitivity of 0.647, and accuracy of 0.809. We concluded that machine learning has great potential for advanced prediction of chronic disease exacerbations based on home telemonitoring data.