Artificial intelligence is part of our daily life and machine learning techniques offer possibilities unknown until now in medicine. This study aims to offer an evaluation of the performance of machine learning (ML) techniques, for predicting bacterial resistance in a urology department.
Data were retrieved from laboratory information system (LIS) concerning 239 patients with urolithiasis hospitalized in the urology department of a tertiary hospital over a 1-year period (2019): age, gender, Gram stain (positive, negative), bacterial species, sample type, antibiotics and antimicrobial susceptibility. In our experiments, we compared several classifiers following a tenfold cross-validation approach on 2 different versions of our dataset; the first contained only information of Gram stain, while the second had knowledge of bacterial species.
The best results in the balanced dataset containing Gram stain, achieve a weighted average receiver operator curve (ROC) area of 0.768 and F-measure of 0.708, using a multinomial logistic regression model with a ridge estimator. The corresponding results of the balanced dataset, that contained bacterial species, achieve a weighted average ROC area of 0.874 and F-measure of 0.783, with a bagging classifier.
Artificial intelligence technology can be used for making predictions on antibiotic resistance patterns when knowing Gram staining with an accuracy of 77% and nearly 87% when identifying specific microorganisms. This knowledge can aid urologists prescribing the appropriate antibiotic 24-48 h before test results are known.
World journal of urology. 2022 May 26 [Epub ahead of print]
Lazaros Tzelves, Lazaros Lazarou, Georgios Feretzakis, Dimitris Kalles, Panagiotis Mourmouris, Evangelos Loupelis, Spyridon Basourakos, Marinos Berdempes, Ioannis Manolitsis, Iraklis Mitsogiannis, Andreas Skolarikos, Ioannis Varkarakis
2nd Department of Urology, Sismanogleio General Hospital, National and Kapodistrian University of Athens, Sismanogleiou 37, Marousi, 15126, Athens, Greece., School of Science and Technology, Hellenic Open University, Patras, Greece., Information Technologies Department, Sismanogleio General Hospital, Marousi, Greece., Department of Urology, New York Presbyterian Hospital/Weill Cornell Medicine, New York, NY, USA., 2nd Department of Urology, Sismanogleio General Hospital, National and Kapodistrian University of Athens, Sismanogleiou 37, Marousi, 15126, Athens, Greece. .
PubMed http://www.ncbi.nlm.nih.gov/pubmed/35616713