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dc.contributor.author | Oliveira, Tiago | es_ES |
dc.contributor.author | Silva, Ana | es_ES |
dc.contributor.author | Satoh, Ken | es_ES |
dc.contributor.author | Julian Inglada, Vicente Javier | es_ES |
dc.contributor.author | Leao, Pedro | es_ES |
dc.contributor.author | Novais, Paulo | es_ES |
dc.date.accessioned | 2020-06-12T03:32:50Z | |
dc.date.available | 2020-06-12T03:32:50Z | |
dc.date.issued | 2018-09 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/146155 | |
dc.description.abstract | [EN] Prediction in health care is closely related with the decision-making process. On the one hand, accurate survivability prediction can help physicians decide between palliative care or other practice for a patient. On the other hand, the notion of remaining lifetime can be an incentive for patients to live a fuller and more fulfilling life. This work presents a pipeline for the development of survivability prediction models and a system that provides survivability predictions for years one to five after the treatment of patients with colon or rectal cancer. The functionalities of the system are made available through a tool that balances the number of necessary inputs and prediction performance. It is mobile-friendly and facilitates the access of health care professionals to an instrument capable of enriching their practice and improving outcomes. The performance of survivability models was compared with other existing works in the literature and found to be an improvement over the current state of the art. The underlying system is capable of recalculating its prediction models upon the addition of new data, continuously evolving as time passes. | es_ES |
dc.description.sponsorship | The work of Tiago Oliveira was supported by JSPS KAKENHI Grant Number JP18K18115. | 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 | Survivability prediction | es_ES |
dc.subject | Clinical decision support | es_ES |
dc.subject | Machine learning | es_ES |
dc.subject.classification | LENGUAJES Y SISTEMAS INFORMATICOS | es_ES |
dc.title | Survivability Prediction of Colorectal Cancer Patients: A System with Evolving Features for Continuous Improvement | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.3390/s18092983 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/JSPS//JP18K18115/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació | es_ES |
dc.description.bibliographicCitation | Oliveira, T.; Silva, A.; Satoh, K.; Julian Inglada, VJ.; Leao, P.; Novais, P. (2018). Survivability Prediction of Colorectal Cancer Patients: A System with Evolving Features for Continuous Improvement. Sensors. 18(9). https://doi.org/10.3390/s18092983 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.3390/s18092983 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 18 | es_ES |
dc.description.issue | 9 | es_ES |
dc.identifier.eissn | 1424-8220 | es_ES |
dc.identifier.pmid | 30200676 | es_ES |
dc.identifier.pmcid | PMC6163414 | es_ES |
dc.relation.pasarela | S\373997 | es_ES |
dc.contributor.funder | Japan Society for the Promotion of Science | es_ES |
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