Uncertainty-Aware Antimicrobial Resistance Prediction in E. coli and S. aureus Isolates Using Hybrid Bayesian Neural Networks

Uncertainty-Aware Antimicrobial Resistance Prediction in E. coli and S. aureus Isolates Using Hybrid Bayesian Neural Networks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Uncertainty-Aware Antimicrobial Resistance Prediction in E. coli and S. aureus Isolates Using Hybrid Bayesian Neural Networks Suman Kumari, Afra Mannan, Hasaan Hamid, Saadia Andleeb, Muhammad Naseer Bajwa, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8732992/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background: Antimicrobial resistance (AMR) poses a critical global health threat. However, conventional detection methods still require up to 72 hours, leading to treatment delays. AI models trained on mass spectrometry data enable faster prediction, but current approaches often lack uncertainty estimation, cross-hospital generalization, and clear interpretability of their predictions. This study proposes a framework for AMR prediction that quantifies uncertainty while enhancing interpretability and generalizability. Methods: We developed species-specific AI models for AMR prediction and incorporated Bayesian inference to estimate predictive uncertainty. Models were trained on the largest DRIAMS subset, corresponding to data from a single hospital. Generalization was evaluated through fine-tuning and zero-shot testing on the remaining three DRIAMS subsets, each originating from a different hospital, as well as on the MS-UMG dataset from a different country. Model interpretability was examined using SHAP-based feature attributions and UMAP visualizations. Results: Proposed AI models outperformed state-of-the-art methods. On held-out testing from the source DRIAMS dataset, the models achieved AUROC and AUPRC values of up to 0.90, with balanced accuracy reaching up to 0.80. Performance remained strong under distribution shift, attaining AUROC and AUPRC values of up to 0.88 and balanced accuracy up to 0.78 following fine-tuning on data from other hospitals, and up to 0.81 AUROC/AUPRC with balanced accuracy up to 0.71 in zero-shot evaluation. Bayesian AI models matched this performance while providing meaningful uncertainty estimates, which were found to be higher for misclassified samples. SHAP and UMAP analyses revealed biologically meaningful representations, with UMAP showing clear separation across most antibiotics and SHAP indicating consistent feature importance within same antibiotic classes for each species. Conclusion: This study demonstrates that proposed AI models generalize well across different datasets. Elevated uncertainty for misclassifications indicates that the models appropriately flagged ambiguous cases. These findings suggest that our approach represents an important step towards building more robust and trustworthy AMR prediction systems for clinical and surveillance applications. AI in Healthcare Antimicrobial Resistance Deep Learning Computer-Aided Diagnosis Proteomics Bioinformatics Multi-Label Classification Mass Spectrometry Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 09 Feb, 2026 Reviewers agreed at journal 09 Feb, 2026 Reviewers agreed at journal 06 Feb, 2026 Reviewers agreed at journal 06 Feb, 2026 Reviewers invited by journal 06 Feb, 2026 Editor assigned by journal 02 Feb, 2026 Submission checks completed at journal 02 Feb, 2026 First submitted to journal 29 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8732992","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588639626,"identity":"d378d0fc-df23-410f-98e4-cbbe340a7014","order_by":0,"name":"Suman Kumari","email":"","orcid":"","institution":"National University of Sciences and Technology","correspondingAuthor":false,"prefix":"","firstName":"Suman","middleName":"","lastName":"Kumari","suffix":""},{"id":588639628,"identity":"e90e6611-e6cc-4eea-8480-1bfd320a7d9a","order_by":1,"name":"Afra Mannan","email":"","orcid":"","institution":"National University of Sciences and Technology","correspondingAuthor":false,"prefix":"","firstName":"Afra","middleName":"","lastName":"Mannan","suffix":""},{"id":588639633,"identity":"0defbba4-5597-4845-b841-584cc028e293","order_by":2,"name":"Hasaan Hamid","email":"","orcid":"","institution":"National University of Sciences and Technology","correspondingAuthor":false,"prefix":"","firstName":"Hasaan","middleName":"","lastName":"Hamid","suffix":""},{"id":588639634,"identity":"aa67294f-484f-4be8-9bce-53abef44c9aa","order_by":3,"name":"Saadia Andleeb","email":"","orcid":"","institution":"National University of Sciences and Technology","correspondingAuthor":false,"prefix":"","firstName":"Saadia","middleName":"","lastName":"Andleeb","suffix":""},{"id":588639637,"identity":"10c92591-3902-4359-9a8a-af04b80c4cda","order_by":4,"name":"Muhammad Naseer Bajwa","email":"data:image/png;base64,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","orcid":"","institution":"National University of Sciences and Technology","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"Naseer","lastName":"Bajwa","suffix":""},{"id":588639639,"identity":"8fb0c94b-220a-4cd1-a32f-d714dcefb22e","order_by":5,"name":"Muhammad Moazam Fraz","email":"","orcid":"","institution":"National University of Sciences and Technology","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Moazam","lastName":"Fraz","suffix":""}],"badges":[],"createdAt":"2026-01-29 15:24:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8732992/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8732992/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102368731,"identity":"08156e64-488c-4a9b-9a97-a8d4e51691bd","added_by":"auto","created_at":"2026-02-11 03:21:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2126486,"visible":true,"origin":"","legend":"","description":"","filename":"AMRprediction.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8732992/v1_covered_c340bb47-a343-45fe-9a4a-c3eb15b98ebf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Uncertainty-Aware Antimicrobial Resistance Prediction in E. coli and S. aureus Isolates Using Hybrid Bayesian Neural Networks","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-informatics-and-decision-making","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"midm","sideBox":"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/midm/default.aspx","title":"BMC Medical Informatics and Decision Making","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"AI in Healthcare, Antimicrobial Resistance, Deep Learning, Computer-Aided Diagnosis, Proteomics, Bioinformatics, Multi-Label Classification, Mass Spectrometry","lastPublishedDoi":"10.21203/rs.3.rs-8732992/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8732992/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003eAntimicrobial resistance (AMR) poses a critical global health threat. However, conventional detection methods still require up to 72 hours, leading to treatment delays. AI models trained on mass spectrometry data enable faster prediction, but current approaches often lack uncertainty estimation, cross-hospital generalization, and clear interpretability of their predictions. This study proposes a framework for AMR prediction that quantifies uncertainty while enhancing interpretability and generalizability.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eWe developed species-specific AI models for AMR prediction and incorporated Bayesian inference to estimate predictive uncertainty. Models were trained on the largest DRIAMS subset, corresponding to data from a single hospital. Generalization was evaluated through fine-tuning and zero-shot testing on the remaining three DRIAMS subsets, each originating from a different hospital, as well as on the MS-UMG dataset from a different country. Model interpretability was examined using SHAP-based feature attributions and UMAP visualizations.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eProposed AI models outperformed state-of-the-art methods. On held-out testing from the source DRIAMS dataset, the models achieved AUROC and AUPRC values of up to 0.90, with balanced accuracy reaching up to 0.80. Performance remained strong under distribution shift, attaining AUROC and AUPRC values of up to 0.88 and balanced accuracy up to 0.78 following fine-tuning on data from other hospitals, and up to 0.81 AUROC/AUPRC with balanced accuracy up to 0.71 in zero-shot evaluation. Bayesian AI models matched this performance while providing meaningful uncertainty estimates, which were found to be higher for misclassified samples. SHAP and UMAP analyses revealed biologically meaningful representations, with UMAP showing clear separation across most antibiotics and SHAP indicating consistent feature importance within same antibiotic classes for each species.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e\u003cp\u003eThis study demonstrates that proposed AI models generalize well across different datasets. Elevated uncertainty for misclassifications indicates that the models appropriately flagged ambiguous cases. These findings suggest that our approach represents an important step towards building more robust and trustworthy AMR prediction systems for clinical and surveillance applications.\u003c/p\u003e","manuscriptTitle":"Uncertainty-Aware Antimicrobial Resistance Prediction in E. coli and S. aureus Isolates Using Hybrid Bayesian Neural Networks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-11 03:21:14","doi":"10.21203/rs.3.rs-8732992/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-02-10T02:10:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"120529444990830326113897901704818247542","date":"2026-02-10T01:43:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"76892699042882188917964442581646046443","date":"2026-02-06T14:19:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"275889542910582513333127871279338449018","date":"2026-02-06T13:29:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-06T12:18:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-02T11:49:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-02T11:48:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Informatics and Decision Making","date":"2026-01-29T15:04:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-informatics-and-decision-making","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"midm","sideBox":"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/midm/default.aspx","title":"BMC Medical Informatics and Decision Making","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dbad2422-04ae-4b63-bb78-99609b481241","owner":[],"postedDate":"February 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-11T03:21:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-11 03:21:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8732992","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8732992","identity":"rs-8732992","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}