Antimicrobial Resistance Prediction in Intensive Care Unit for Pseudomonas Aeruginosa using Temporal Data-Driven Models

One threatening medical problem for human beings is the increasing antimicrobial resistance of some microorganisms. This problem is especially difficult in Intensive Care Units (ICUs) of hospitals due to the vulnerable state of patients. Knowing in advance whether a concrete bacterium is resistant or susceptible to an antibiotic is a crux step for clinicians to determine an effective antibiotic treatment. This usual clinical procedure takes approximately 48 hours and it is named antibiogram. It tests the bacterium resistance to one or more antimicrobial families (six of them considered in this work). This article focuses on cultures of the Pseudomonas Aeruginosa bacterium because is one of the most dangerous in the ICU. Several temporal data-driven models are proposed and analyzed to predict the resistance or susceptibility to a determined antibiotic family previously to know the antibiogram result and only using the available past information from a data set. This data set is formed by anonymized electronic health records data from more than 3300 ICU patients during 15 years. Several data-driven classifier methods are used in combination with several temporal modeling approaches. The results show that our predictions are reasonably accurate for some antimicrobial families, and could be used by clinicians to determine the best antibiotic therapy in advance. This early prediction can save valuable time to start the adequate treatment for an ICU patient. This study corroborates the results of a previous work pointing that the antimicrobial resistance of bacteria in the ICU is related to other recent resistance tests of ICU patients. This information is very valuable for making accurate antimicrobial resistance predictions

Predicting multi-resistance of bacteria in an Intensive Care Unit

Treball fi de màster de: Master in Intelligent Interactive SystemsTutors: Miquel Sànchez i Marrè, Vicenç GómezThis 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.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)