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.

通过研究慢性病发作中的病征危机的预测，可以在病征发生之前做出决策，如服用药物以避免症状或激活医疗警报。本研究展示了针对偏头痛这一慢性疾病病征危机的预测限制，并提出了一种构建预测模型并改进初始模型预测能力的方法。实验结果显示了一个接近40分钟的预测范围，与药物的药代动力学时间范围相符。在无侵入式无线体感传感网络布置的日常临床研究中获得数据的实际场景下进行了实验，结果为疾病发生病征危机的预测算法的未来研究范围选择提供了有效的方法论。

慢性疾病中症状事件准确及及时预测的建模方法论