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Dynamic Models Supporting Personalised Chronic Disease Management through Healthcare Sensors with Interactive Process Mining

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Dynamic Models Supporting Personalised Chronic Disease Management through Healthcare Sensors with Interactive Process Mining

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dc.contributor.author Valero Ramon, Zoe es_ES
dc.contributor.author Fernández Llatas, Carlos es_ES
dc.contributor.author Valdivieso, Bernardo es_ES
dc.contributor.author Traver Salcedo, Vicente es_ES
dc.date.accessioned 2021-05-27T03:34:52Z
dc.date.available 2021-05-27T03:34:52Z
dc.date.issued 2020-09 es_ES
dc.identifier.uri http://hdl.handle.net/10251/166838
dc.description.abstract [EN] Rich streams of continuous data are available through Smart Sensors representing a unique opportunity to develop and analyse risk models in healthcare and extract knowledge from data. There is a niche for developing new algorithms, and visualisation and decision support tools to assist health professionals in chronic disease management incorporating data generated through smart sensors in a more precise and personalised manner. However, current understanding of risk models relies on static snapshots of health variables or measures, rather than ongoing and dynamic feedback loops of behaviour, considering changes and different states of patients and diseases. The rationale of this work is to introduce a new method for discovering dynamic risk models for chronic diseases, based on patients¿ dynamic behaviour provided by health sensors, using Process Mining techniques. Results show the viability of this method, three dynamic models have been discovered for the chronic diseases hypertension, obesity, and diabetes, based on the dynamic behaviour of metabolic risk factors associated. This information would support health professionals to translate a one-fits-all current approach to treatments and care, to a personalised medicine strategy, that fits treatments built on patients¿ unique behaviour thanks to dynamic risk modelling taking advantage of the amount data generated by smart sensors. es_ES
dc.description.sponsorship This research was partially funded by the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement no. 727560. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Sensors es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Process mining es_ES
dc.subject Interactive es_ES
dc.subject Dynamic models es_ES
dc.subject Chronic diseases es_ES
dc.subject Obesity es_ES
dc.subject Hypertension es_ES
dc.subject Hyperglycemia es_ES
dc.subject Smart sensors es_ES
dc.subject.classification TECNOLOGIA ELECTRONICA es_ES
dc.title Dynamic Models Supporting Personalised Chronic Disease Management through Healthcare Sensors with Interactive Process Mining es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/s20185330 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/727560/EU/Collective wisdom driving public health policies/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica es_ES
dc.description.bibliographicCitation Valero Ramon, Z.; Fernández Llatas, C.; Valdivieso, B.; Traver Salcedo, V. (2020). Dynamic Models Supporting Personalised Chronic Disease Management through Healthcare Sensors with Interactive Process Mining. Sensors. 20(18):1-25. https://doi.org/10.3390/s20185330 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/s20185330 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 25 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 20 es_ES
dc.description.issue 18 es_ES
dc.identifier.eissn 1424-8220 es_ES
dc.identifier.pmid 32957673 es_ES
dc.identifier.pmcid PMC7570892 es_ES
dc.relation.pasarela S\418748 es_ES
dc.contributor.funder European Commission es_ES
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