A Machine Learning Model for Prediction of Antibiotic Resistance with Escherichia Coli Infections Using Demographic, Clinical and Microbiological Data.

Abstract

IntroductionIn, low- and middle-income countries like Uganda, there is growing reliance on empirical prescription of broad-spectrum antibiotics which, while targeting a wide range of pathogens, contributes to the development of resistance to common pathogens such as E. coli. This challenge is compounded by the poor selection of antibiotic panels in many laboratories, which often fail to reflect local resistance patterns and patient-specific factors, leading to inefficient use of scarce resources and delayed appropriate treatment. Objectives of the study The objectives of this study were to; 1) identify risk factors for drug resistant E. coli infections using machine learning techniques; 2) evaluate the performance of different machine learning models in predicting the likelihood of drug resistance among patients with E. coli infections using demographic, clinical and microbiological data; and 3) develop a web-based interface to support proper antibiotic prescription and targeted antimicrobial decision-making. MethodologyA retrospective analysis was conducted on 1,552 records of patients diagnosed with E. coli infections in 10 tertiary healthcare facilities in Uganda. These records were analyzed using machine learning models including Lightgbm, xgboost, random forest, gradient boosting, and decision trees. Feature selection was guided by a weighted importance score and frequency count framework. The best performing model was deployed in a streamlit-based web interface.Results Key predictors of resistance included antibiotic type, patient age, hospital site, specimen type, prior antibiotic use, and hospitalization history. XGboost emerged as the top-performing models for prediction of drug resistance, with an accuracy of 82.32%, a precision of 82.36%, recall of 85.37%, F1 score of 83.84%, and ROC AUC of 90.17%. The web-based interface was implemented using python streamlit technology, intergrated with the best performing model to enable real-time resistance prediction.ConclusionThis study demonstrates the potential of machine learning to transform antimicrobial resistance surveillance and clinical decision-making in resource-limited settings.