RT Journal Article
T1 Modeling methodology for the accurate and prompt prediction of symptomatic events in chronic diseases
A1 Pagán Ortiz, Josué
A1 Risco Martín, José Luis
A1 Moya, José M.
A1 Ayala Rodrigo, José Luis
AB 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 40 min, 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.
PB Elsevier
SN 1532-0464
YR 2016
FD 2016-05-30
LK https://hdl.handle.net/20.500.14352/94872
UL https://hdl.handle.net/20.500.14352/94872
LA eng
NO Ministerio de Economía, Comercio y Empresa (España)
DS Docta Complutense
RD 6 abr 2025