[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120830-en":3,"doc-seo-120830-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120830,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Interpretable machine learning-based decision support for prediction of antibiotic resistance for complicated urinary tract infections","Antimicrobial resistance in bacterial pathogens makes it difficult for clinicians to select appropriate antibiotics for patients with potentially complicated urinary tract infections (UTIs). The study develops four interpretable machine learning decision support algorithms that use electronic health record data to predict resistance across nitrofurantoin, co-trimoxazole, ciprofloxacin, and levofloxacin. Results show strong predictability in a large complicated-UTI cohort and evidence of generalizability to an uncomplicated-UTI cohort. Interpretability is emphasized to explain the basis for model predictions, supporting faster, personalized clinical intervention and reducing non-susceptible treatments.","[www.nature.com/npjamar](www.nature.com/npjamar)  \nARTICLE OPEN   \nInterpretable machine learning-based decision support for prediction of antibiotic resistance for complicated urinary tract infections  \nJenny Yang 1 ✉ , David W. Eyre2, Lei Lu1 and David A. Clifton1,3  \n|  |  |  |\n| --- | --- | --- |\n|  | Urinary tract infections are one of the most common bacterial infections worldwide; however, increasing antimicrobial resistance in bacterial pathogens is making it challenging for clinicians to correctly prescribe patients appropriate antibiotics. In this study, we present four interpretable machine learning-based decision support algorithms for predicting antimicrobial resistance. Using electronic health record data from a large cohort of patients diagnosed with potentially complicated UTIs, we demonstrate high predictability of antibiotic resistance across four antibiotics – nitrofurantoin, co-trimoxazole, ciprofloxacin, and levofloxacin. We additionally demonstrate the generalizability of our methods on a separate cohort of patients with uncomplicated UTIs, demonstrating that machine learning-driven approaches can help alleviate the potential of administering non-susceptible treatments, facilitate rapid effective clinical interventions, and enable personalized treatment suggestions Additionally, these |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n| .\u003Cbr>techniques present the benefit of providing model interpretability, explaining the basis for generated predictions. |  |  |\n|  | npj Antimicrobials & Resistance (2023)1:14; [https://doi.org/10.1038/s44259-023-00015-2](https://doi.org/10.1038/s44259-023-00015-2) |  |\n|  |  |  |\n\nINTRODUCTION  \nRecent years have seen rapid increases in the prevalence of antimicrobial resistance in bacterial pathogens, which is threatening the efficacy of many antibiotic therapies, and ultimately leading to treatment failure1–3. Although new drugs are urgently needed, new antibiotic development is restricted by costs, limited government support, and regulatory requirements1,2. For instance, as of 2019, major pharmaceutical corporations, commonly known as “big pharma,” were progressively divesting themselves of antibiotic research and development (R&D) assets4. This shift restricts the opportunities available to smaller companies and their investors, leading to heightened financial constraints and a lack of infrastructure for antibiotic R&D.  \nFurthermore, antibiotic resistance leads to increased reliance on broad-spectrum therapies, which select for further resistance, exacerbating the issue at hand3,5. To avoid these risks, it is critical for clinicians to accurately align available antibiotic therapies with the precise susceptibilities of bacterial pathogens. Ideally, this alignment should occur when initiating empirical treatment, even before culture results are obtained (which might take several days to be available) . In this study, we present interpretable machine learning (ML)-based methods for predicting antimicrobial resistance (AMR), which decreases the risk of non-susceptible and therefore, ineffective treatment, and facilitates rapid effective clinical intervention. We demonstrate the utility of these systems for urinary tract infections (UTIs), where the problem of antibiotic resistance is of particular importance.  \nUTIs are one of the most common bacterial infections worldwide, affecting more than 150 million people each year3,6. The pathogens that cause UTIs, including Escherichia coli, Klebsiella pneumoniae, Proteus mirabilis, Enterococcus faecalis and Staphylococcus saprophyticus3,6,7 can be carried asymptomatically and thus, are frequently exposed to antibiotics, including those intended for other infections2. This exposure, combined with high recurrence  \nrates, often results in multidrug-resistant strains, with resistance rates of over 20% for commonly used drugs3. As treatmentoutcome is associated with the infecting pathogen’s susceptibilit","cbCaiq5QFPkmcHLQ","https://ap.wps.com/l/cbCaiq5QFPkmcHLQ","pdf",1181545,1,9,"English","en",105,"# Introduction\n## Antimicrobial resistance challenges and need for decision support\n## Interpretable ML for predicting antimicrobial resistance in UTIs\n## Prior work using EHR-based ML approaches","[{\"question\":\"What problem does the study address in urinary tract infections?\",\"answer\":\"Rising antimicrobial resistance makes it challenging to prescribe effective antibiotics quickly and accurately for UTIs.\"},{\"question\":\"How do the proposed methods support clinical prescribing?\",\"answer\":\"They use electronic health record data to predict which antibiotics are likely to be susceptible, enabling faster identification of effective interventions.\"},{\"question\":\"Why is interpretability important in these decision support algorithms?\",\"answer\":\"Interpretability provides explanations for the basis of generated predictions, helping clinicians understand model-driven recommendations.\"}]","Interpretable machine learning-based decision support for prediction of antibiotic resistance for complicated urinary tract infections | 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problem does the study address in urinary tract infections?","Question",{"text":75,"@type":76},"Rising antimicrobial resistance makes it challenging to prescribe effective antibiotics quickly and accurately for UTIs.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do the proposed methods support clinical prescribing?",{"text":80,"@type":76},"They use electronic health record data to predict which antibiotics are likely to be susceptible, enabling faster identification of effective interventions.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is interpretability important in these decision support algorithms?",{"text":84,"@type":76},"Interpretability provides explanations for the basis of generated predictions, helping clinicians understand model-driven 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