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dc.contributor.author | Arnal-Benedicto, Laura![]() |
es_ES |
dc.contributor.author | Pons-Suñer, Pedro![]() |
es_ES |
dc.contributor.author | Navarro Cerdan, José Ramón![]() |
es_ES |
dc.contributor.author | Ruiz Valls, Pablo![]() |
es_ES |
dc.contributor.author | Caballero Mateos, Mª Jose![]() |
es_ES |
dc.contributor.author | Valdivieso Martínez, Bernardo![]() |
es_ES |
dc.contributor.author | Perez-Cortes, Juan-Carlos![]() |
es_ES |
dc.date.accessioned | 2023-05-11T18:02:17Z | |
dc.date.available | 2023-05-11T18:02:17Z | |
dc.date.issued | 2022-07-15 | es_ES |
dc.identifier.issn | 1932-6203 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/193284 | |
dc.description.abstract | [EN] Unplanned hospital readmissions mean a significant burden for health systems. Accurately estimating the patient's readmission risk could help to optimise the discharge decision-making process by smartly ordering patients based on a severity score, thus helping to improve the usage of clinical resources. A great number of heterogeneous factors can influence the readmission risk, which makes it highly difficult to be estimated by a human agent. However, this score could be achieved with the help of AI models, acting as aiding tools for decision support systems. In this paper, we propose a machine learning classification and risk stratification approach to assess the readmission problem and provide a decision support system based on estimated patient risk scores. | es_ES |
dc.description.sponsorship | L.A., P.P.S, J.R.N.C, P.R.V. and J.C.P.C. were founded by Generalitat Valenciana through IVACE (Valencian Institute of Business Competitiveness, https://www.ivace.es/index.php/es/) distributed nominatively to Valencian technological innovation centres under project IMDEEA-2021-100. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Public Library of Science | es_ES |
dc.relation.ispartof | PLoS ONE | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Medical Risk Factors | es_ES |
dc.subject | Hospitalization | es_ES |
dc.subject | Machine Learning | es_ES |
dc.subject | Decision Making | es_ES |
dc.subject | Economic Analysis | es_ES |
dc.subject | Trees | es_ES |
dc.subject.classification | ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES | es_ES |
dc.subject.classification | ESTADISTICA E INVESTIGACION OPERATIVA | es_ES |
dc.title | Decision support through risk cost estimation in 30-day hospital unplanned readmission | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1371/journal.pone.0271331 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/Institut Valencià de Competitivitat Empresarial//IMDEEA%2F2021%2F100//BIGSALUD3. Análisis de Datos e Inteligencia Artificial para optimización del sistema de salud/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica | es_ES |
dc.description.bibliographicCitation | Arnal-Benedicto, L.; Pons-Suñer, P.; Navarro Cerdan, JR.; Ruiz Valls, P.; Caballero Mateos, MJ.; Valdivieso Martínez, B.; Perez-Cortes, J. (2022). Decision support through risk cost estimation in 30-day hospital unplanned readmission. PLoS ONE. 17(7):1-16. https://doi.org/10.1371/journal.pone.0271331 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1371/journal.pone.0271331 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 16 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 17 | es_ES |
dc.description.issue | 7 | es_ES |
dc.identifier.pmid | 35839222 | es_ES |
dc.identifier.pmcid | PMC9286269 | es_ES |
dc.relation.pasarela | S\469484 | es_ES |
dc.contributor.funder | Institut Valencià de Competitivitat Empresarial | es_ES |