Open AccessRapid 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,
Yu-Ching Liu,
DerYang Cho,
Jia-Wen 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) - workflow , methicillin resistant staphylococcus aureus , staphylococcus aureus , usability , identification (biology) , matrix assisted laser desorption/ionization , artificial intelligence , machine learning , medicine , computer science , biology , chemistry , bacteria , desorption , database , botany , human–computer interaction , genetics , organic chemistry , adsorption
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.