Modeling methodology for the accurate and prompt prediction of symptomatic events in chronic diseases

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dc.contributor.authorPagán Ortiz, Josué
dc.contributor.authorRisco Martín, José Luis
dc.contributor.authorMoya, José M.
dc.contributor.authorAyala Rodrigo, José Luis
dc.date.accessioned2024-01-23T16:30:20Z
dc.date.available2024-01-23T16:30:20Z
dc.date.issued2016-05-30
dc.description.abstractPrediction 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.
dc.description.departmentDepto. de Arquitectura de Computadores y Automática
dc.description.facultyFac. de Informática
dc.description.refereedTRUE
dc.description.sponsorshipMinisterio de Economía, Comercio y Empresa (España)
dc.description.statuspub
dc.identifier.doi10.1016/j.jbi.2016.05.008
dc.identifier.issn1532-0464
dc.identifier.officialurlhttps://www.doi.org/10.1016/j.jbi.2016.05.008
dc.identifier.relatedurlhttps://www.sciencedirect.com/science/article/pii/S1532046416300429
dc.identifier.urihttps://hdl.handle.net/20.500.14352/94872
dc.journal.titleJournal Of Biomedical Informatics
dc.language.isoeng
dc.page.final147
dc.page.initial136
dc.publisherElsevier
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO//TEC2012-33892/ES/TECNOLOGIAS HW%2FSW PARA LA EFICIENCIA ENERGETICA EN SISTEMAS DE COMPUTACION DISTRIBUIDOS/
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.accessRightsrestricted access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.keywordMigraine
dc.subject.keywordWBSN
dc.subject.keywordModeling
dc.subject.keywordState-space
dc.subject.keywordIdentification
dc.subject.keywordPrediction
dc.subject.keywordFeature
dc.subject.ucmInteligencia artificial (Informática)
dc.subject.unesco1203.04 Inteligencia Artificial
dc.titleModeling methodology for the accurate and prompt prediction of symptomatic events in chronic diseases
dc.typejournal article
dc.volume.number62
dspace.entity.typePublication
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relation.isAuthorOfPublicationb18c2bd8-52be-4d79-bd8b-dbd8e970d703
relation.isAuthorOfPublicationd73a810d-34c3-440e-8b5f-e2a7b0eb538f
relation.isAuthorOfPublication.latestForDiscovery2e4c4d42-c8d8-450e-bf6b-28f327b89a44
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