Rapid identification of carbapenem-resistant Klebsiella pneumoniae based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry and an artificial neural network model.
Yu-Ming ZhangMei-Fen TsaoChing-Yu ChangKuan-Ting LinJoseph Jordan KellerHsiu-Chen LinPublished in: Journal of biomedical science (2023)
Compared with the prior MALDI-TOF and machine learning studies of CRKP, the amount of data in our study was more sufficient and allowing us to conduct external validation. With better generalization abilities, our artificial neural network model can serve as a reliable screening tool for CRKP isolates in clinical practice. Integrating our model into the current workflow of clinical laboratories can assist the rapid identification of CRKP before the completion of traditional antimicrobial susceptibility testing. The combination of MADLI-TOF MS and machine learning techniques can support physicians in selecting suitable antibiotics, which has the potential to enhance the patients' outcomes and lower the prevalence of antimicrobial resistance.
Keyphrases
- neural network
- machine learning
- antimicrobial resistance
- klebsiella pneumoniae
- mass spectrometry
- clinical practice
- escherichia coli
- big data
- multidrug resistant
- end stage renal disease
- primary care
- ejection fraction
- risk factors
- loop mediated isothermal amplification
- newly diagnosed
- artificial intelligence
- electronic health record
- weight loss
- ms ms
- metabolic syndrome
- chronic kidney disease
- prognostic factors
- type diabetes
- adipose tissue
- case control
- patient reported
- climate change
- human health