Font Gouveia, Arthur2023-04-192023-04-192022-06-12https://hdl.handle.net/2445/196961Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2022, Director: Laura Igual Muñoz[en] Healthcare data availability is growing recently due to the digitalization of clinical records. Therefore, this large amount of data is being used by researchers to improve decision-making process, resources allocation and to address several issues. The aim of this Bachelor’s thesis is to investigate if unsupervised clustering of patients could be helpful to improve predictive models performance for mortality and length of stay in the Intensive Care Unit. The data used belongs to the open source database called MIMIC-III (Medical Information Mart for Intensive Care III). Results shows that clustering prior to predictive models training improved accuracy for the most significant cluster.50 p.application/pdfspamemòria: cc-nc-nd (c) Arthur Font Gouveia, 2022codi: GPL (c) Arthur Font Gouveia, 2022http://www.gnu.org/licenses/gpl-3.0.ca.htmlhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/Històries clíniquesPresa de decisions (Estadística)ProgramariTreballs de fi de grauUnitats de cures intensivesAprenentatge automàticMedical recordsStatistical decisionComputer softwareIntensive care unitsMachine learningBachelor's thesesinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/openAccess