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Complementary frailty and mortality prediction models on older patients as a tool for assessing palliative care needs

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Complementary frailty and mortality prediction models on older patients as a tool for assessing palliative care needs

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dc.contributor.author Blanes-Selva, Vicent es_ES
dc.contributor.author Doñate-Martínez, Ascensión es_ES
dc.contributor.author Linklater, Gordon es_ES
dc.contributor.author Garcia-Gomez, Juan M es_ES
dc.date.accessioned 2023-10-19T18:01:25Z
dc.date.available 2023-10-19T18:01:25Z
dc.date.issued 2022-04 es_ES
dc.identifier.issn 1460-4582 es_ES
dc.identifier.uri http://hdl.handle.net/10251/198408
dc.description.abstract [EN] Palliative care (PC) has demonstrated benefits for life-limiting illnesses. Bad survival prognosis and patients' decline are working criteria to guide PC decision-making for older patients. Still, there is not a clear consensus on when to initiate early PC. This work aims to propose machine learning approaches to predict frailty and mortality in older patients in supporting PC decision-making. Predictive models based on Gradient Boosting Machines (GBM) and Deep Neural Networks (DNN) were implemented for binary 1-year mortality classification, survival estimation and 1-year frailty classification. Besides, we tested the similarity between mortality and frailty distributions. The 1-year mortality classifier achieved an Area Under the Curve Receiver Operating Characteristic (AUC ROC) of 0.87 [0.86, 0.87], whereas the mortality regression model achieved an mean absolute error (MAE) of 333.13 [323.10, 342.49] days. Moreover, the 1-year frailty classifier obtained an AUC ROC of 0.89 [0.88, 0.90]. Mortality and frailty criteria were weakly correlated and had different distributions, which can be interpreted as these assessment measurements are complementary for PC decision-making. This study provides new models that can be part of decision-making systems for PC services in older patients after their external validation. es_ES
dc.description.sponsorship The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the InAdvance project (H2020-SC1-BHC-2018-2020 No. 825750). es_ES
dc.language Inglés es_ES
dc.publisher SAGE Publications es_ES
dc.relation.ispartof Health Informatics Journal es_ES
dc.rights Reconocimiento - No comercial (by-nc) es_ES
dc.subject Palliative care es_ES
dc.subject Machine learning es_ES
dc.subject Deep learning es_ES
dc.subject Frailty es_ES
dc.subject Mortality es_ES
dc.subject Older patients es_ES
dc.subject Needs assessment es_ES
dc.subject.classification FISICA APLICADA es_ES
dc.title Complementary frailty and mortality prediction models on older patients as a tool for assessing palliative care needs es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1177/14604582221092592 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/825750/EU es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros Industriales - Escola Tècnica Superior d'Enginyers Industrials es_ES
dc.description.bibliographicCitation Blanes-Selva, V.; Doñate-Martínez, A.; Linklater, G.; Garcia-Gomez, JM. (2022). Complementary frailty and mortality prediction models on older patients as a tool for assessing palliative care needs. Health Informatics Journal. 28(2):1-18. https://doi.org/10.1177/14604582221092592 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1177/14604582221092592 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 18 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 28 es_ES
dc.description.issue 2 es_ES
dc.identifier.pmid 35642719 es_ES
dc.relation.pasarela S\466233 es_ES
dc.contributor.funder COMISION DE LAS COMUNIDADES EUROPEA es_ES
upv.costeAPC 1210 es_ES


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