arXiv Machine Learning
Routine Blood Tests Outperform CRP for Distinguishing Bacterial From Viral Infection in Children
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arXiv Machine Learning
Oct 28
ARMD-Predict: multi-site machine learning to predict antibiotic resistance before culture results
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.
arXiv Machine Learning
Sep 16
Benchmarking Machine Learning Architectures for Antimicrobial Stewardship in Pediatric ICUs
arXiv:2605.22611v2 Announce Type: replace Abstract: Antimicrobial stewardship (AMS) is critical in pediatric intensive care units (PICUs), where diagnostic uncertainty often drives broad-spectrum ant...
arXiv Machine Learning
Sep 10
Machine Learning for Pre-Culture ESBL Risk Stratification to Guide Empiric Antibiotic Selection: A 12-Hospital Study of Enterobacteriaceae Cultures
The study presents a cost‑sensitive XGBoost model that predicts ESBL‑producing Enterobacteriaceae at the time of culture ordering using 45 pre‑culture EHR features from 132,955 cultures across 12 hospitals. At 90% sensitivity the model achieves a 95.8% negative predictive value, potentially allowing carbapenem‑sparing therapy in non‑ICU settings while avoiding unnecessary broad‑spectrum treatment for 307 out of every 1,000 cultures, at the expense of 14 missed ESBL cases per 1,000. SHAP analysis highlights prior ESBL colonization as the strongest predictor, with minimal performance loss when socioeconomic deprivation features are omitted, supporting equitable bedside deployment.
arXiv Machine Learning
Jul 7
LeukocyteCount: Automatic Identification and Counting for leukocytes using Deep Learning
arXiv:2607. 04486v1 Announce Type: new Abstract: Diagnosing and monitoring diseases frequently involves the analysis of human biological samples, with blood analysis being pivotal.
arXiv Machine Learning
Jun 16
Forecasting Bacterial Antimicrobial Resistance Trends Using Machine Learning on WHO GLASS Surveillance Data: A Retrieval-Augmented Generation Approach for Policy Decision Support
arXiv:2602. 22673v2 Announce Type: replace Abstract: Background: Antimicrobial resistance (AMR) is a global health threat.
arXiv AI
Jun 26
Explainable Ensemble-Based Machine Learning Models for Detecting the Presence of Cirrhosis in Hepatitis C Patients
arXiv:2606. 26561v1 Announce Type: new Abstract: Hepatitis C is a liver infection caused by a virus, which results in mild to severe inflammation of the liver.