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Automatic analysis of high dimensional categorical variables in medical databases for the prediction of hospital bacteremia

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dc.contributor.advisorGarnica Alcázar, Óscar
dc.contributor.advisorRuiz Giardín, José Manuel
dc.contributor.authorRey García, Jaime del
dc.date.accessioned2023-06-17T10:57:12Z
dc.date.available2023-06-17T10:57:12Z
dc.date.issued2021
dc.degree.titleGrado en Ingeniería Informática
dc.descriptionTrabajo de Fin de Grado en Ingeniería Informática, Facultad de Informática UCM, Departamento de Arquitectura de Computadores y Automática, Curso 2020/2021.
dc.description.abstractThis project aims to continue and consolidate the study for the bacteriemia detection process and its diagnosis carried out by some faculty companions last year. A first glance through the analysis of numerical variables allowed a deeper understanding and the trace of an approach for a quick detection model. Now, categorical variables take relevance too in order to successfully achieve higher results in the classifier models. The addition of categorical variables in classifier models has been around for at least five years due to the increase in computational capacity, and the benefits in the classifiers as direct consequence is clear. Yet, it is proven that, as complex and abstract as language is, classifiers do struggle when data with slang or abbreviations comes up for prediction, even if its linguistic register is heavily bounded, i.e. when strictly related to medical issues data is treated. Throughout the study we will apply text cleaning and text processing methods to prepare the variables for use, since their format is heterogeneous and unsuitable to be processed by Machine Learning tools. We will also apply the string similarity method to identify all those classes that can help in the algorithm classification process and we will assess the most suitable types of encoding for working with these variables. Finally, we will apply the Random Forest Machine Learning algorithm on the set with techniques that allow us to avoid data learning bias and we will assess the results in terms of the success rates and the relevance of the variables in the decision-making process of the algorithm.
dc.description.departmentDepto. de Arquitectura de Computadores y Automática
dc.description.facultyFac. de Informática
dc.description.refereedTRUE
dc.description.statusunpub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/74572
dc.identifier.urihttps://hdl.handle.net/20.500.14352/10603
dc.language.isoeng
dc.page.total65
dc.rightsAtribución-NoComercial 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttps://creativecommons.org/licenses/by-nc/3.0/es/
dc.subject.cdu004(043.3)
dc.subject.keywordBacteremia
dc.subject.keywordComorbidity
dc.subject.keywordPredictive medicine
dc.subject.keywordPathogenesis
dc.subject.keywordDataframe
dc.subject.keywordDirty category
dc.subject.keywordString similarity
dc.subject.keywordOne hot encoding
dc.subject.keywordAdjacency matrix
dc.subject.keywordAdjacency list
dc.subject.keywordBinary encoding
dc.subject.keywordK-Nearest Neighbors (KNN)
dc.subject.keywordBias and Variance
dc.subject.keywordK-Fold Cross Validation
dc.subject.keywordRandom forest
dc.subject.keywordROC
dc.subject.keywordSHAP
dc.subject.ucmInformática (Informática)
dc.subject.unesco1203.17 Informática
dc.titleAutomatic analysis of high dimensional categorical variables in medical databases for the prediction of hospital bacteremia
dc.title.alternativeAnálisis automático de variables categóricas de alta dimensionalidad en bases de datos médicas para la predicción de bacteriemias hospitalarias
dc.typebachelor thesis
dspace.entity.typePublication
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