Predicting multi-resistance of bacteria in an Intensive Care Unit

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  • dc.contributor.author Hernàndez Carnerero, Àlvar
  • dc.date.accessioned 2021-02-05T09:30:53Z
  • dc.date.available 2021-02-05T09:30:53Z
  • dc.date.issued 2020-07
  • dc.description Treball fi de màster de: Master in Intelligent Interactive Systemsca
  • dc.description Tutors: Miquel Sànchez i Marrè, Vicenç Gómez
  • dc.description.abstract This study considers the prediction of “multi-drug” resistance (MDR) of Pseudomonas aeruginosa bacterium caused by nosocomial infections in the Intensive Care Unit (ICU). An ensemble of binary classifiers implemented with different Machine Learning (ML) methods is applied for prediction using as training data health records and past sensitivity tests (antibiogram) results. This work proposes to generate two new types of features to improve predictor’s performance. The first one is based on using information of previous antibiograms of a particular patient to predict their future resistance to antibiotics. The second kind of features employs bacterial information from the rest of the patients in the ICU to predict the antimicrobial resistance for a certain patient. In addition, in the study it is suggested to use a training window with incremental size so that training set is always temporarily as near as possible to the test instances to be predicted. Some techniques such as feature selection and oversampling are also used to further improve efficiency and accuracy. Results show that using an incremental window for training improves success rates in the domain of this problem, and expose that knowing the outcomes of past antibiograms, substantially improves prediction. It is also observed that considering resistant bacteria present in the ICU is useful to anticipate antimicrobial resistance. From these results it is further inferred that resistant bacteria may be spreading among patients in the ICU within populations that rapidly mutate, which can induce non-stationary in the data distribution. It is concluded that using these contributions, experiments show promising results in MDR prediction even using simple features and limited training data.ca
  • dc.description.sponsorship This work has been partly supported by the Spanish Thematic Network “Learning Machines for Singular Problems and Applications (MAPAS)” (TIN2017-90567-REDT, MINECO/FEDER EU).
  • dc.format.mimetype application/pdf*
  • dc.identifier.uri http://hdl.handle.net/10230/46363
  • dc.language.iso engca
  • dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/TIN2017-90567-REDT
  • dc.rights Reconeixement-NoComercial-SenseObraDerivada 4.0 Internacional (CC BY-NC-ND 4.0)ca
  • dc.rights.accessRights info:eu-repo/semantics/openAccessca
  • dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/ca
  • dc.subject.keyword Temporal modelling
  • dc.subject.keyword Machine learning
  • dc.subject.keyword Classification problem
  • dc.subject.keyword Clinical data
  • dc.subject.keyword Antibiogram
  • dc.subject.keyword Antimicrobial resistance
  • dc.subject.keyword Intensive care unit
  • dc.subject.keyword Health care
  • dc.title Predicting multi-resistance of bacteria in an Intensive Care Unitca
  • dc.type info:eu-repo/semantics/masterThesisca