Application of Machine Learning Algorithms in Urinary Tract Infections Diagnosis Based on Non-Microbiological Parameters

Abstract

Urinary tract infections (UTIs) are among the most common pathologies, with a high incidence in women and hospitalized patients. Their diagnosis is based on the presence of clinical symptoms and signs in addition to the detection of microorganisms in urine trough urine cultures, a time-consuming and resource-intensive test. The goal was to optimize UTI detection through artificial intelligence (machine learning) using non-microbiological laboratory parameters, thereby reducing unnecessary cultures and expediting diagnosis. A total of 4283 urine cultures from patients with suspected UTIs were analyzed in the Microbiology Laboratory of the University Hospital Virgen de las Nieves (Granada, Spain) between 2016 and 2020. Various machine learning algorithms were applied to predict positive urine cultures and the type of isolated microorganism. Random Forest demonstrated the best performance, achieving an accuracy (percentage of correct positive and negative classifications) of 82.2% and an area under the ROC curve of 87.1%. Moreover, the Tree algorithm successfully predicted the presence of Gram-negative bacilli in urine cultures with an accuracy of 79.0%. Among the most relevant predictive variables were the presence of leukocytes and nitrites in the urine dipstick test, along with elevated white cells count, monocyte count, lymphocyte percentage in blood and creatinine levels. The integration of AI algorithms and non-microbiological parameters within the diagnostic and management pathways of UTI holds considerable promise. However, further validation with clinical data is required for integration into hospital practice.