{
  "abstract": "Clinicians select empiric antibiotics before culture susceptibility results are available. Electronic health record (EHR) models could inform that choice, but most are developed at a single hospital and transport poorly to others. We developed ARMD-Predict using 7.5 million culture-antibiotic observations from four academic medical centers. One set of models estimates susceptibility to individual antibiotics; a second, hierarchical set predicts the infecting organism and then organism-specific resistance phenotypes. Across 23 pooled susceptibility models evaluated in same-period, patient-grouped test partitions, median area under the receiver operating characteristic curve (AUROC) was 0.77 for inpatient urine and 0.70 for inpatient blood cultures; chronological retraining reduced AUROC by approximately 0.04. At an exploratory threshold with 95% positive predictive value, derived and evaluated on the same test observations, the model identified 55% of ceftriaxone-susceptible inpatient urine observations as suitable for narrower therapy and flagged 60% of ceftriaxone-resistant observations for broader coverage. Organism classifiers achieved top-3 accuracy of 51.3-83.3%; phenotype AUROCs were conditional on the observed organism. Research applications are available at https://armd-predict.stanford.edu. Independent calibration, threshold and end-to-end prospective validation are required before clinical use.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.32998",
  "author_total": 20,
  "authors": [
    {
      "name": "Fateme Nateghi Haredasht"
    },
    {
      "name": "Nicholas P. Marshall"
    },
    {
      "name": "Wenyuan Chen"
    },
    {
      "name": "Lauren N. Cooper"
    },
    {
      "name": "Ziming Wei"
    },
    {
      "name": "Harrison Konsker"
    },
    {
      "name": "Jacob S. Chen"
    },
    {
      "name": "Michael Klompas"
    },
    {
      "name": "Cara McKenna"
    },
    {
      "name": "Richard J. Medford"
    },
    {
      "name": "Sanjat Kanjilal"
    },
    {
      "name": "Austin J. Schoeffler"
    },
    {
      "name": "Stephen P. Ma"
    },
    {
      "name": "Niaz Banaei"
    },
    {
      "name": "Steven M. Asch"
    },
    {
      "name": "Amy Chang"
    },
    {
      "name": "Stanley C. Deresinski"
    },
    {
      "name": "Mary K. Goldstein"
    },
    {
      "name": "Robert Tibshirani"
    },
    {
      "name": "Jonathan H. Chen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/25ft4r65e0zwd.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.32998",
  "fulltext_url": null,
  "id": "25ft4r65e0zwd",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.32998",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "q-bio.QM"
    ],
    [
      "openalex",
      "1702|Machine Learning in Healthcare"
    ]
  ],
  "openalex": "W7223855658",
  "pdf_url": "https://arxiv.org/pdf/2610.32998",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/25ft4r65e0zwd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:44Z",
      "native_id": "2610.32998",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.32998"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    }
  ],
  "title": "ARMD-Predict: multi-site machine learning to predict antibiotic resistance before culture results",
  "title_text": "ARMD-Predict: multi-site machine learning to predict antibiotic resistance before culture results",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:44Z",
  "url": "https://research.draconitic.com/paper/25ft4r65e0zwd",
  "venue": "arXiv",
  "versions": []
}