Modeling methodology for the accurate and prompt prediction of symptomatic events in chronic diseases.
Journal of Biomedical Informatics（2016）
Display Omitted A methodology to predict symptomatic events in chronic diseases is proposed.Predictions are improved in terms of prediction and future horizon.Feature and model selection techniques to provide an average prediction are show.The methodology is applied in a real ambulatory scenario to predict migraines.Prediction times in the range of the pharmacokinetics of drugs are achieved. Prediction of symptomatic crises in chronic diseases allows to take decisions before the symptoms occur, such as the intake of drugs to avoid the symptoms or the activation of medical alarms. The prediction horizon is in this case an important parameter in order to fulfill the pharmacokinetics of medications, or the time response of medical services. This paper presents a study about the prediction limits of a chronic disease with symptomatic crises: the migraine. For that purpose, this work develops a methodology to build predictive migraine models and to improve these predictions beyond the limits of the initial models. The maximum prediction horizon is analyzed, and its dependency on the selected features is studied. A strategy for model selection is proposed to tackle the trade off between conservative but robust predictive models, with respect to less accurate predictions with higher horizons. The obtained results show a prediction horizon close to 40min, which is in the time range of the drug pharmacokinetics. Experiments have been performed in a realistic scenario where input data have been acquired in an ambulatory clinical study by the deployment of a non-intrusive Wireless Body Sensor Network. Our results provide an effective methodology for the selection of the future horizon in the development of prediction algorithms for diseases experiencing symptomatic crises.更多