Rapid Identification of Methicillin-Resistant Staphylococcus aureus Using MALDI-TOF MS and Machine Learning from over 20,000 Clinical Isolates | Zendy

Jiaxin Yu | Zendy; Ni Tien | Zendy; YuChing Liu | Zendy; DerYang Cho | Zendy; Jiawen Chen | Zendy; Yin-Tai Tsai | Zendy; Yu-Chen Huang | Zendy; Huei-Jen Chao | Zendy; ChaoJung Chen | Zendy

Open Access

Rapid Identification of Methicillin-Resistant Staphylococcus aureus Using MALDI-TOF MS and Machine Learning from over 20,000 Clinical Isolates

Author(s) -

Jiaxin Yu,

Ni Tien,

YuChing Liu,

DerYang Cho,

Jiawen Chen,

Yin-Tai Tsai,

Yu-Chen Huang,

Huei-Jen Chao,

ChaoJung Chen

Publication year - 2022

Publication title -

microbiology spectrum

Language(s) - English

Resource type - Journals

ISSN - 2165-0497

DOI - 10.1128/spectrum.00483-22

Subject(s) - staphylococcus aureus , usability , identification (biology) , methicillin resistant staphylococcus aureus , medicine , artificial intelligence , microbiology and biotechnology , computer science , computational biology , biology , bacteria , genetics , human–computer interaction , ecology

Over 20,000 clinical MSSA and MRSA isolates were collected to build a machine learning (ML) model to identify MSSA/MRSA and their markers. This model was tested across four external clinical sites to ensure the model’s usability.

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