ArticleInfection and drug resistance2024
Artificial Intelligence-Clinical Decision Support System in Infectious Disease Control: Combatting Multidrug-Resistant
Article in Infection and drug resistance, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review of machine learning on clinical MALDI-TOF MS.Briefings in bioinformatics · 2026Pooled it
- Harnessing AI for advancing pathogenic microbiology: a bibliometric and topic modeling approach.Frontiers in microbiology · 2024Pooled it
- Artificial intelligence for antimicrobial resistance: advancing reproducibility, interpretability, and clinical deployment.Briefings in bioinformatics · 2026Review
- Review
- Machine Learning Model for Predicting Multidrug Resistance in ClinicalDiagnostics (Basel, Switzerland) · 2026Article
- Observational
- Research advances in the application of microfluidic chip technology for rapid detection of antibiotic-resistant bacteria.Frontiers in cellular and infection microbiology · 2026Review
- Artificial Intelligence for Antimicrobial Resistance Detection and Prediction inInfection and drug resistance · 2026Review
- Development and validation of a hierarchical machine learning method using MALDI-TOF mass spectrometry for rapid SCCmec typing and PVL detection in MRSA: a multi-centre study.Emerging microbes & infections · 2025Article
- Interpretable machine learning of clinical MALDI-TOF spectra discriminates carbapenem-resistantJournal of mass spectrometry and advances in the clinical lab · 2025Article
- MALDI-TOF MS in conjunction with machine learning: toward a new era for antimicrobial susceptibility testing.Frontiers in cellular and infection microbiology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: The World Health Organization has identified Patients and Methods: We selected 4307 KP samples out of a total of 52,827 bacterial samples due to concerns about multi-drug resistance using MALDI-TOF MS and Vitek-2 systems for AST. It involved thorough data preprocessing, feature extraction, and machine learning model training fine-tuned with GridSearchCV and 5-fold cross-validation, resulting in high predictive accuracy, as demonstrated by the receiver operating characteristic and area under the curve (AUC) scores, laying the groundwork for our AI-CDSS. Results: MALDI-TOF MS analysis revealed distinct intensity profiles differentiating CRKP and susceptible strains, as well as colistin-resistant Conclusion: Integrating MALDI-TOF MS with machine learning in an AI-CDSS has greatly expedited the detection of KP resistance by approximately 1 day. This system offers timely guidance, potentially enhancing clinical decision-making and improving treatment outcomes for KP infections.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.