This paper is in the following e-collection/theme issue:

Formative Evaluation of Digital Health Interventions (3300) Data Science (363) Machine Learning (2129) Nosocomial Infections (30) Perioperative Infection Control and Prevention (10) Natural Language Processing (931) Antimicrobial Stewardship and Antibiotic Resistance (62)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/54044, first published .
Predictive Model for Extended-Spectrum β-Lactamase–Producing Bacterial Infections Using Natural Language Processing Technique and Open Data in Intensive Care Unit Environment: Retrospective Observational Study

Predictive Model for Extended-Spectrum β-Lactamase–Producing Bacterial Infections Using Natural Language Processing Technique and Open Data in Intensive Care Unit Environment: Retrospective Observational Study

Predictive Model for Extended-Spectrum β-Lactamase–Producing Bacterial Infections Using Natural Language Processing Technique and Open Data in Intensive Care Unit Environment: Retrospective Observational Study

Authors of this article:

Genta Ito1 Author Orcid Image ;   Shuntaro Yada1 Author Orcid Image ;   Shoko Wakamiya1 Author Orcid Image ;   Eiji Aramaki1 Author Orcid Image
Article Authors Cited by (3) Tweetations (1) Metrics

Journals

  1. Shanaa M, Abdallah S. A Hybrid Anomaly Detection Framework Combining Supervised and Unsupervised Learning for Credit Card Fraud Detection. F1000Research 2025;14:664 View
  2. Shanaa M, Abdallah S. A Hybrid Anomaly Detection Framework Combining Supervised and Unsupervised Learning for Credit Card Fraud Detection. F1000Research 2025;14:664 View
  3. He X, Wan Q, Zhou Y, Yan J, Zhao Y, Tang F. Designing novel FAK inhibitors targeting gastric cancer: a combined approach using machine learning, docking analysis, molecular dynamics simulations, and experimental validation. Journal of Computer-Aided Molecular Design 2026;40(1) View