  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 minutes, 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.
