ArticleAntibiotics (Basel, Switzerland)2021
Artificial Intelligence to Get Insights of Multi-Drug Resistance Risk Factors during the First 48 Hours from ICU Admission.
Article in Antibiotics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- The Predictive Value of Nomogram Model Based on Neutrophil-to-Lymphocyte Ratio (NLR) and Prothrombin Time-International Normalized Ratio (PT-INR)-to-Albumin Ratio (PTAR) for Early Bacterial Intra-Abdominal Infection After Orthotopic Liver Transplantation.Journal of inflammation research · 2026Article
- Predictive modelling of hospital-acquired infection in acute ischemic stroke using machine learning.Scientific reports · 2024Article
- Ensemble feature selection and tabular data augmentation with generative adversarial networks to enhance cutaneous melanoma identification and interpretability.BioData mining · 2024Article
- From Data to Decisions: Leveraging Artificial Intelligence and Machine Learning in Combating Antimicrobial Resistance - a Comprehensive Review.Journal of medical systems · 2024Review
- Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Complementary supramolecular drug associates in perfecting the multidrug therapy against multidrug resistant bacteria.Frontiers in immunology · 2024Article
- A deep learning model for predicting multidrug-resistant organism infection in critically ill patients.Journal of intensive care · 2023Article
- The Interventions and Challenges of Antimicrobial Stewardship in the Emergency Department.Antibiotics (Basel, Switzerland) · 2023Review
- Artificial Intelligence in Infection Management in the ICU.Critical care (London, England) · 2022Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Multi-drug resistance (MDR) is one of the most current and greatest threats to the global health system nowadays. This situation is especially relevant in Intensive Care Units (ICUs), where the critical health status of these patients makes them more vulnerable. Since MDR confirmation by the microbiology laboratory usually takes 48 h, we propose several artificial intelligence approaches to get insights of MDR risk factors during the first 48 h from the ICU admission. We considered clinical and demographic features, mechanical ventilation and the antibiotics taken by the patients during this time interval. Three feature selection strategies were applied to identify statistically significant differences between MDR and non-MDR patient episodes, ending up in 24 selected features. Among them, SAPS III and Apache II scores, the age and the department of origin were identified. Considering these features, we analyzed the potential of machine learning methods for predicting whether a patient will develop a MDR germ during the first 48 h from the ICU admission. Though the results presented here are just a first incursion into this problem, artificial intelligence approaches have a great impact in this scenario, especially when enriching the set of features from the electronic health records.
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