Identifying diabetic patient profile through machine learning-based clustering analysis

dc.contributor.authorGomes, Joãopor
dc.contributor.authorLopes, Joãopor
dc.contributor.authorGuimarães, Tiago André Saraivapor
dc.contributor.authorSantos, Manuelpor
dc.date.accessioned2024-03-22T11:33:13Z
dc.date.available2024-03-22T11:33:13Z
dc.date.issued2023
dc.description.abstractGiven the rapid growth over the past 15 years, Diabetes is currently a key issue in medical science and healthcare administration. Considering the importance of the health sector in our society, it is critical to correctly diagnose and treat Diabetes in order to avoid immediate difficulties and reduce the chance of long-term issues. The analysis of vast amounts of data that are available in organizations is an important factor to describing their internal factors, predicting future trends, and prescribing the best course of action to improve their performance in light of the increasing technological evolution and the emergence of Artificial Intelligence (AI). The main objective of this project, which is being carried out in collaboration with the Unidade Local de Saúde do Alto Minho (ULSAM), is to define a typology of diabetic patients by building Machine Learning (ML) models from registered clinical information, medication, complementary diagnostic tools, therapeutic and monitoring data, and registered medication data.por
dc.description.sponsorship(undefined)por
dc.distributioninternationalpor
dc.identifier.citationGomes, J., Lopes, J., Guimarães, T., & Santos, M. F. (2023). Identifying Diabetic Patient Profile Through Machine Learning-Based Clustering Analysis. Procedia Computer Science. Elsevier BV. http://doi.org/10.1016/j.procs.2023.03.116
dc.identifier.doi10.1016/j.procs.2023.03.116por
dc.identifier.issn1877-0509
dc.identifier.urihttps://hdl.handle.net/1822/89850
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherElsevierpor
dc.rightsopenAccesspor
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/por
dc.subjectClusteringpor
dc.subjectDiabetespor
dc.subjectMachine learningpor
dc.subject.fosEngenharia e Tecnologia::Outras Engenharias e Tecnologiaspor
dc.subject.odsSaúde de qualidadepor
dc.titleIdentifying diabetic patient profile through machine learning-based clustering analysispor
dc.typeconferencePaperpor
dspace.entity.typePublicationen
oaire.citationConferencePlaceLeuven, Belgiumpor
oaire.citationEndPage867por
oaire.citationStartPage862por
oaire.citationVolume220por
oaire.versionVoRpor
sdum.conferencePublicationThe 4th International Workshop on Hospital 4.0 (Hospital) March 15-17, 2023por
sdum.journalProcedia Computer Sciencepor

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