arXiv Machine Learning

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 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.

By Aravind V. Kuruvikkattil, Lalitha Pranathi Pulavarthy, Rashmita Kudamala, Saptarshi Purkayastha

arXiv Machine Learning

Sep 21

Routine Blood Tests Outperform CRP for Distinguishing Bacterial From Viral Infection in Children

arXiv:2609.21332v1 Announce Type: new Abstract: Acute infectious diseases are among the leading causes of medical consultations and hospitalizations in children worldwide. These infections are predom...

By Mihaela Demireva, Zhecho Mitev, Djuna Chinareva-Klimentova, Svetoslav Ivanov, Georgi Nalbantov, Dimitar Mitev

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...

By Niklas Raehse, Luregn J. Schlapbach, Daphn\'e Chopard

benchmarks

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.

By Md Tanvir Hasan Turja

rag safety

arXiv Machine Learning

Jul 28

Metamorphic Testing for Clinical ML Models: A Framework Proposal and Pilot Study

arXiv:2607. 22984v1 Announce Type: cross Abstract: Machine learning models for clinical prediction tasks, such as in-hospital mortality and sepsis onset, routinely achieve high AUROC scores.

By Jie JW Wu, Feiyu E, Bo Chen

arXiv Machine Learning

23h ago

Machine Learning for Live-Birth Prediction After IVF/ICSI From Structured Clinical Data: A Systematic Review, Meta-Analysis, and Critical Appraisal

This systematic review and meta‑analysis examined 48 machine‑learning studies that predict live‑birth outcomes after IVF/ICSI using routine clinical data. The pooled discrimination of internally validated models was an AUC of 0.814, while externally validated models had an AUC of 0.756, with no significant difference between them. Most studies (47/48) were at high or unclear risk of bias, only four assessed calibration, and none demonstrated clinical impact, leading to a low‑to‑very‑low certainty of evidence.

By Abdul Raheem Shahzad, Muhammad Zaheer Khan, Umar Ajaib, Asad Ur Rehman, Juan Deng, Ming Zhang

benchmarks safety

arXiv AI

Jun 2

RISED: A Pre-Deployment Evaluation Framework for High-Stakes AI Decision-Support Systems, with Application to Healthcare

arXiv:2605. 12895v2 Announce Type: replace-cross Abstract: Clinical decision-support systems are expert systems whose recommendations clinicians act on directly, yet they are usually cleared on one aggregate accuracy number from a held-out test set.

By Rohith Reddy Bellibatlu, Manpreet Singh, Yash Jajoo, Shyamal Lakhanpal, Abhishek Israni

arXiv AI

Jun 26

KG-TRACE: A Neuro-Symbolic Framework for Mechanistic Grounding in Antimicrobial Resistance Prediction

arXiv:2606. 26179v1 Announce Type: cross Abstract: While WGS-based AMR prediction has reached high accuracy, existing models lack a mechanism to ground neural attributions in established biological pathways.

By Naman Garg, Sarika Jain, Sourav Yadav, Bharat K. Bhargava, Ghanapriya Singh, Abhishek Srivastava, Parimal Kar

rag safety

arXiv AI

Sep 10

Fusing Sequence Motifs and Pan-Genomic Features: Antimicrobial Resistance Prediction using an Explainable Lightweight 1D CNN-XGBoost Ensemble

arXiv:2509.23552v2 Announce Type: replace-cross Abstract: Antimicrobial Resistance (AMR) is a rapidly escalating global health crisis. While genomic sequencing enables rapid prediction of resistance...

By Md. Saiful Bari Siddiqui, Nowshin Tarannum

llms benchmarks

arXiv AI

Aug 11

FoMoH: A clinically meaningful foundation model evaluation for structured electronic health records

arXiv:2505. 16941v4 Announce Type: replace-cross Abstract: Foundation models (FMs) promise to address core limitations of traditional supervised machine learning: (i) reliance on large amounts of labeled data, (ii) task specificity, and (iii) poor transportability.

By Vincent Jeanselme, Zilin Jing, Aparajita Kashyap, Chao Pang, Florent Pollet, Young Sang Choi, Xinzhuo Jiang, Yuta Kobayashi, Yanwei Li, Sara Matijevic, Karthik Natarajan, Shalmali Joshi

benchmarks safety

arXiv Machine Learning

Aug 4

Development and Validation of a Dynamic Kidney Failure Prediction Model based on Deep Learning: A Real-World Study with External Validation

arXiv:2501. 16388v3 Announce Type: replace Abstract: Background: Chronic kidney disease (CKD), a progressive disease with high morbidity and mortality, has become a significant global public health problem.

By Jingying Ma, Jinwei Wang, Lanlan Lu, Zhiqin Jiang, Mengling Feng, Feifei Zhang, Peng Shen, Yexiang Sun, Shenda Hong, Luxia Zhang

safety

arXiv AI

Aug 11

Hit Selection Using SSMD-Based Machine Learning Performance Metrics in High-Throughput Screening Assays

arXiv:2608. 07609v1 Announce Type: cross Abstract: High-throughput screening (HTS) assays are central to early-stage drug discovery but are often limited by extreme data sparsity, as primary screens typically use only a single replicate per test substance.

By Xiaohua Douglas Zhang

efficiency

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