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Prediction of A. Baumannii Amikacin Resistance in Clinical Metagenomics.
- Source :
-
Studies in health technology and informatics [Stud Health Technol Inform] 2024 Aug 22; Vol. 316, pp. 212-213. - Publication Year :
- 2024
-
Abstract
- Respiratory tract infections are a serious threat to health, especially in the presence of antimicrobial resistance (AMR). Existing AMR detection methods are limited by slow turnaround times and low accuracy due to the presence of false positives and negatives. In this study, we simulate 1,116 clinical metagenomics samples on both Illumina and Nanopore sequencing from curated, real-world sequencing of A. baumannii respiratory infections and build AI models to predict resistance to amikacin. The best performance is achieved by XGBoost on Illumina sequencing (area under the ROC curve = 0.7993 on 5-fold cross-validation).
- Subjects :
- Humans
Respiratory Tract Infections drug therapy
Respiratory Tract Infections microbiology
Anti-Bacterial Agents pharmacology
Anti-Bacterial Agents therapeutic use
Acinetobacter Infections drug therapy
Acinetobacter Infections microbiology
Amikacin pharmacology
Amikacin therapeutic use
Metagenomics
Acinetobacter baumannii drug effects
Acinetobacter baumannii genetics
Drug Resistance, Bacterial genetics
Subjects
Details
- Language :
- English
- ISSN :
- 1879-8365
- Volume :
- 316
- Database :
- MEDLINE
- Journal :
- Studies in health technology and informatics
- Publication Type :
- Academic Journal
- Accession number :
- 39176710
- Full Text :
- https://doi.org/10.3233/SHTI240381