By Vijay Kumar MalesuNov 6 2023Reviewed by Lily Ramsey, LLM In a recent study published in npj Antimicrobials and Resistance, researchers developed and validated interpretable machine learning algorithms that predict antibiotic resistance in complicated urinary tract infections , enhancing clinical decision-making and promoting personalized treatments.
Given the growing threat of AMR and reduced antibiotic research and development by major pharma firms, there is a need for further research for tools such as interpretable ML to predict resistance and steer effective treatments for ailments like UTIs. Related StoriesFor consistency with prior research, the researchers adopted a similar data structure and filtering technique, and each data point consisted of urine specimens analyzed for AMR with details such as the antimicrobial susceptibility profile, past specimen features for AMR prediction, and basic patient information.
The dataset could not determine if patients had conditions such as asymptomatic bacteriuria , which could influence the study's outcomes.