Accurate prediction of antimicrobial resistance and genetic marker of Staphylococcus aureus clinical isolates using MALDI-TOF MS and machine learning - across DRIAMS and Taiwan database.

Journal: International journal of antimicrobial agents
PMID: 39244164

Abstract

BACKGROUND: The use of matrix-assisted laser desorption/ionisation-time-of-flight mass spectra (MALDI-TOF MS) with machine learning (ML) has been explored for predicting antimicrobial resistance. This study evaluates the effectiveness of MALDI-TOF MS paired with various ML classifiers and establishes optimal models for predicting antimicrobial resistance and the presence of mecA gene among Staphylococcus aureus.

Authors

  • Wei-Yao Wang
    School of Medicine, Chung Shan Medical University, Taichung, Taiwan; Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan.
  • Chen-Feng Chiu
    Department of Internal Medicine, Feng Yuan Hospital, Ministry of Health and Welfare, Taichung, Taiwan.
  • Shih-Ming Tsao
    School of Medicine, Chung Shan Medical University, Taichung, Taiwan; Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan.
  • Yu-Lin Lee
    School of Medicine, Chung Shan Medical University, Taichung, Taiwan; Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan.
  • Yi-Hsin Chen
    Department of Nephrology, Taichung Tzu Chi Hospital, Taichung, Taiwan; School of Medicine, Tzu Chi University, Hualien, Taiwan; Department of Artificial Intelligence and Data Science, National Chung Hsing University, Taichung, Taiwan. Electronic address: yishin0819@gmail.com.

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