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dc.contributor.authorHaq, Anam
dc.contributor.authorWilk, Szymon
dc.contributor.authorAbelló Gamazo, Alberto
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Serveis i Sistemes d'Informació
dc.date.accessioned2019-06-13T06:47:53Z
dc.date.available2019-06-13T06:47:53Z
dc.date.issued2019-03-29
dc.identifier.citationHaq, A.; Wilk, S.; Abelló, A. Fusion of clinical data: A case study to predict the type of treatment of bone fractures. "International journal of applied mathematics and computer science", 29 Març 2019, vol. 29, núm. 1, p. 51-67.
dc.identifier.issn1641-876X
dc.identifier.urihttp://hdl.handle.net/2117/134366
dc.description.abstractA prominent characteristic of clinical data is their heterogeneity—such data include structured examination records and laboratory results, unstructured clinical notes, raw and tagged images, and genomic data. This heterogeneity poses a formidable challenge while constructing diagnostic and therapeutic decision models that are currently based on single modalities and are not able to use data in different formats and structures. This limitationmay be addressed using data fusion methods. In this paper, we describe a case study where we aimed at developing data fusion models that resulted in various therapeutic decision models for predicting the type of treatment (surgical vs. non-surgical) for patients with bone fractures. We considered six different approaches to integrate clinical data: one fusion model based on combination of data (COD) and five models based on combination of interpretation (COI). Experimental results showed that the decision model constructed following COI fusion models is more accurate than decision models employing COD. Moreover, statistical analysis using the one-way ANOVA test revealed that there were two groups of constructed decision models, each containing the set of three different models. The results highlighted that the behavior of models within a group can be similar, although it may vary between different groups.
dc.format.extent17 p.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica
dc.subjectÀrees temàtiques de la UPC::Informàtica::Sistemes d'informació
dc.subject.lcshBones -- Wounds and injuries -- Diagnosis
dc.subject.lcshDecision support systems
dc.subject.lcshMedical records -- Data processing
dc.subject.otherClinical data
dc.subject.otherData fusion
dc.subject.otherCombination of data
dc.subject.otherCombination of interpretation
dc.subject.otherPrediction models
dc.titleFusion of clinical data: A case study to predict the type of treatment of bone fractures
dc.typeArticle
dc.subject.lemacOssos -- Ferides i lesions -- Diagnòstic
dc.subject.lemacSistemes d'ajuda a la decisió
dc.subject.lemacHistòries clíniques -- Informàtica
dc.contributor.groupUniversitat Politècnica de Catalunya. inSSIDE - integrated Software, Service, Information and Data Engineering
dc.identifier.doi10.2478/amcs-2019-0004
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://content.sciendo.com/view/journals/amcs/29/1/article-p51.xml
dc.rights.accessOpen Access
local.identifier.drac25179525
dc.description.versionPostprint (published version)
local.citation.authorHaq, A.; Wilk, S.; Abelló, A.
local.citation.publicationNameInternational journal of applied mathematics and computer science
local.citation.volume29
local.citation.number1
local.citation.startingPage51
local.citation.endingPage67


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Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain