Interpretable machine learning-based decision support for prediction of antibiotic resistance for complicated urinary tract infections
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 machin...
| Main Authors: | , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Springer Nature
2023
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| _version_ | 1826313633510981632 |
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| author | Yang, J Eyre, DW Lu, L Clifton, DA |
| author_facet | Yang, J Eyre, DW Lu, L Clifton, DA |
| author_sort | Yang, J |
| collection | OXFORD |
| description | 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 techniques present the benefit of providing model interpretability, explaining the basis for generated predictions. |
| first_indexed | 2024-03-07T08:13:19Z |
| format | Journal article |
| id | oxford-uuid:e39fd8b0-da11-459e-9781-13f56a9bba54 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-09-25T04:17:59Z |
| publishDate | 2023 |
| publisher | Springer Nature |
| record_format | dspace |
| spelling | oxford-uuid:e39fd8b0-da11-459e-9781-13f56a9bba542024-07-20T14:58:45ZInterpretable machine learning-based decision support for prediction of antibiotic resistance for complicated urinary tract infectionsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:e39fd8b0-da11-459e-9781-13f56a9bba54EnglishSymplectic ElementsSpringer Nature2023Yang, JEyre, DWLu, LClifton, DAUrinary 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 techniques present the benefit of providing model interpretability, explaining the basis for generated predictions. |
| spellingShingle | Yang, J Eyre, DW Lu, L Clifton, DA Interpretable machine learning-based decision support for prediction of antibiotic resistance for complicated urinary tract infections |
| title | Interpretable machine learning-based decision support for prediction of antibiotic resistance for complicated urinary tract infections |
| title_full | Interpretable machine learning-based decision support for prediction of antibiotic resistance for complicated urinary tract infections |
| title_fullStr | Interpretable machine learning-based decision support for prediction of antibiotic resistance for complicated urinary tract infections |
| title_full_unstemmed | Interpretable machine learning-based decision support for prediction of antibiotic resistance for complicated urinary tract infections |
| title_short | Interpretable machine learning-based decision support for prediction of antibiotic resistance for complicated urinary tract infections |
| title_sort | interpretable machine learning based decision support for prediction of antibiotic resistance for complicated urinary tract infections |
| work_keys_str_mv | AT yangj interpretablemachinelearningbaseddecisionsupportforpredictionofantibioticresistanceforcomplicatedurinarytractinfections AT eyredw interpretablemachinelearningbaseddecisionsupportforpredictionofantibioticresistanceforcomplicatedurinarytractinfections AT lul interpretablemachinelearningbaseddecisionsupportforpredictionofantibioticresistanceforcomplicatedurinarytractinfections AT cliftonda interpretablemachinelearningbaseddecisionsupportforpredictionofantibioticresistanceforcomplicatedurinarytractinfections |