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IPOscore: an interactive web-based platform for postoperative surgical complications analysis and prediction in the oncology domain

2022Journal article Restricted access
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dc.contributor.authorMochão, Hugo
dc.contributor.authorGonçalves, Daniel
dc.contributor.authorAlexandre, Leonardo
dc.contributor.authorCastro, Carolina
dc.contributor.authorValério, Duarte
dc.contributor.authorBarahona, Pedro
dc.contributor.authorMoreira-Gonçalves, Daniel
dc.contributor.authorCosta, Paulo M.
dc.contributor.authorHenriques, Rui
dc.contributor.authorSantos, Lúcio L.
dc.contributor.authorCosta, Rafael S.
dc.date.accessioned2022-04-07T16:14:20Z
dc.date.available2022-04-07T16:14:20Z
dc.date.issued2022
dc.description© 2022 Elsevier B.V. All rights reservedpt_PT
dc.description.abstractBackground: The performance of traditional risk score systems to predict (post)-operative outcomes is limited. This weakness reduces confidence in its use to support clinical risk mitigation decisions. However, the rapid growth of health data in the last years offers principles to deal with some of these limitations. In this regard, the data allows the extraction of relevant information for both patients stratification and the rigorous identification of associated risk factors. The patients can then be targeted to specific preoperative optimization programs, thus contributing to the reduction of associated morbidity and mortality. Objectives: The main goal of this work is, therefore, to provide a clinical decision support system (CDSS) based on data-driven modeling methods for surgical risk prediction specific for cancer patients in Portugal. Results: The result is IPOscore, a single web-based platform aimed at being an innovative approach to assist clinical decision-making in the surgical oncology domain. This system includes a database to store/manage the clinical data collected in a structured format, data visualization and analysis tools, and predictive machine learning models to predict postoperative outcomes in cancer patients. IPOscore also includes a pattern mining module based on biclustering to assess the discriminative power of a pattern towards postsurgical outcomes. Additionally, a mobile application is provided to this end. Conclusions: The IPOscore platform is a valuable tool for surgical oncologists not only for clinical data management but also as a preventative and predictive healthcare system. Currently, this clinical support tool is being tested at the Portuguese Institute of Oncology (IPO-Porto), and can be accessed online at https://iposcore.org.pt_PT
dc.description.sponsorshipThis work was supported by Fundação para a Ciência e a Tecnologia (FCT), through IDMEC, under LAETA project (UIDB/50022/2020) and IPOscore with reference (DSAIPA/DS/0042/2018). This work was further supported by the Associate Laboratory for Green Chemistry (LAQV), financed by national funds from FCT/MCTES (UIDB/50006/2020 and UIDP/50006/2020), INESC-ID plurianual (UIDB/50021/2020), the FCT individual PhD grant to LA (2021.07759.BD) and the contract CEECIND/01399/2017 to RSCpt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationComput Methods Programs Biomed. 2022 Mar 14;219:106754pt_PT
dc.identifier.doi10.1016/j.cmpb.2022.106754pt_PT
dc.identifier.eissn1872-7565
dc.identifier.issn0169-2607
dc.identifier.urihttp://hdl.handle.net/10451/52257
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relationAssociate Laboratory of Energy, Transports and Aeronautics
dc.relationAssociated Laboratory for Green Chemistry - Clean Technologies and Processes
dc.relationAssociated Laboratory for Green Chemistry - Clean Technologies and Processes
dc.relationInstituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa
dc.relationLearning prognostic biomarkers from three-dimensional biomedical data of psychiatric disorders
dc.relationNot Available
dc.relation.publisherversionhttps://www.sciencedirect.com/journal/computer-methods-and-programs-in-biomedicinept_PT
dc.subjectCancerpt_PT
dc.subjectData managementpt_PT
dc.subjectData miningpt_PT
dc.subjectDecision support toolpt_PT
dc.subjectIntelligent systems engineeringpt_PT
dc.subjectMachine learningpt_PT
dc.subjectPostsurgical risk stratificationpt_PT
dc.subjectWeb-based platformpt_PT
dc.titleIPOscore: an interactive web-based platform for postoperative surgical complications analysis and prediction in the oncology domainpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleAssociate Laboratory of Energy, Transports and Aeronautics
oaire.awardTitleAssociated Laboratory for Green Chemistry - Clean Technologies and Processes
oaire.awardTitleAssociated Laboratory for Green Chemistry - Clean Technologies and Processes
oaire.awardTitleInstituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa
oaire.awardTitleLearning prognostic biomarkers from three-dimensional biomedical data of psychiatric disorders
oaire.awardTitleNot Available
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50022%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0042%2F2018/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50006%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F50006%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50021%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/OE/2021.07759.BD/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/CEEC IND 2017/CEECIND%2F01399%2F2017%2FCP1462%2FCT0015/PT
oaire.citation.titleComputer Methods and Programs in Biomedicinept_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream3599-PPCDT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStreamOE
oaire.fundingStreamCEEC IND 2017
person.familyNameCosta
person.givenNamePaulo Matos
person.identifier.ciencia-id0415-4404-DDBE
person.identifier.orcid0000-0002-7550-8285
person.identifier.ridABD-1573-2021
person.identifier.scopus-author-id55977165500
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
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project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccesspt_PT
rcaap.typearticlept_PT
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