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Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 Diabetes

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Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 Diabetes

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dc.contributor.author Martinez-Millana, Antonio es_ES
dc.contributor.author Bayo-Monton, Jose Luis es_ES
dc.contributor.author Argente-Pla, María es_ES
dc.contributor.author Fernández Llatas, Carlos es_ES
dc.contributor.author Merino-Torres, Juan Francsico es_ES
dc.contributor.author Traver Salcedo, Vicente es_ES
dc.date.accessioned 2018-06-12T08:20:17Z
dc.date.available 2018-06-12T08:20:17Z
dc.date.issued 2018 es_ES
dc.identifier.uri http://hdl.handle.net/10251/103890
dc.description.abstract [EN] Life expectancy is increasing and, so, the years that patients have to live with chronic diseases and co-morbidities. Type 2 diabetes is one of the most prevalent chronic diseases, specifically linked to being overweight and ages over sixty. Recent studies have demonstrated the effectiveness of new strategies to delay and even prevent the onset of type 2 diabetes by a combination of active and healthy lifestyle on cohorts of mid to high risk subjects. Prospective research has been driven on large groups of the population to build risk scores that aim to obtain a rule for the classification of patients according to the odds for developing the disease. Currently, there are more than two hundred models and risk scores for doing this, but a few have been properly evaluated in external groups and integrated into a clinical application for decision support. In this paper, we present a novel system architecture based on service choreography and hybrid modeling, which enables a distributed integration of clinical databases, statistical and mathematical engines and web interfaces to be deployed in a clinical setting. The system was assessed during an eight-week continuous period with eight endocrinologists of a hospital who evaluated up to 8080 patients with seven different type 2 diabetes risk models implemented in two mathematical engines. Throughput was assessed as a matter of technical key performance indicators, confirming the reliability and efficiency of the proposed architecture to integrate hybrid artificial intelligence tools into daily clinical routine to identify high risk subjects. es_ES
dc.description.sponsorship The authors wish to acknowledge the consortium of the MOSAIC project (funded by the European Commission, Grant No. FP7-ICT 600914) for their commitment during concept development, which led to the development of the research reported in this manuscript
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 Type 2 diabetes es_ES
dc.subject Risk models es_ES
dc.subject Service-oriented architecture es_ES
dc.subject System integration es_ES
dc.subject System reliability pilot es_ES
dc.subject Decision making es_ES
dc.subject Health care es_ES
dc.subject.classification TECNOLOGIA ELECTRONICA es_ES
dc.title Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 Diabetes es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/s18010079 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/600914/EU/MOSAIC - MOdels and Simulation techniques for discovering diAbetes Influence faCtors/
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 Martinez-Millana, A.; Bayo-Monton, JL.; Argente-Pla, M.; Fernández Llatas, C.; Merino-Torres, JF.; Traver Salcedo, V. (2018). Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 Diabetes. Sensors. 18 (1)(79):1-26. https://doi.org/10.3390/s18010079 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://doi.org/10.3390/s18010079 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 26 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 18 (1) es_ES
dc.description.issue 79 es_ES
dc.identifier.eissn 1424-8220 es_ES
dc.identifier.pmid 29286314 en_EN
dc.identifier.pmcid PMC5795558 en_EN
dc.relation.pasarela S\350019 es_ES
dc.contributor.funder European Commission
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