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arXiv Machine Learning By Fateme Nateghi Haredasht, Nicholas P. Marshall, Wenyuan Chen, Lauren N. Cooper, Ziming Wei, Harrison Konsker, Jacob S. Chen, Michael Klompas, Cara McKenna, Richard J. Medford, Sanjat Kanjilal, Austin J. Schoeffler, Stephen P. Ma, Niaz Banaei, Steven M. Asch, Amy Chang, Stanley C. Deresinski, Mary K. Goldstein, Robert Tibshirani, Jonathan H. Chen

ARMD-Predict: multi-site machine learning to predict antibiotic resistance before culture results

Read the original on arXiv Machine Learning →

ARMD‑Predict is a multi‑site machine‑learning system trained on 7.5 million culture‑antibiotic records from four academic medical centers to forecast antibiotic resistance before culture results are available. It offers two tiers of models: one set predicts susceptibility to individual antibiotics, while a hierarchical set first identifies the infecting organism and then its resistance profile. In internal testing, the models achieved median AUROCs of 0.77 for inpatient urine and 0.70 for inpatient blood cultures, and could flag a substantial proportion of ceftriaxone‑susceptible and resistant cases for narrower or broader therapy, respectively.

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