The application of machine learning in clinical microbiology and infectious diseases

Cheng Xu¹Lingyun Zhao²Cunsi Ye³Kechen Xu^4*Keyang Xu^5*

• ¹Clinical Laboratory of Chun’an First People’s Hospital, Zhejiang Provincial People’s Hospital Chun’an Branch, Hangzhou Medical College Affiliated Chun’an Hospital, Hangzhou, Zhejiang, PR China, Hangzhou, China
• ²Department of Medicine & Therapeutics, Prince of Wales Hospital, Hongkong, China
• ³Department of Clinical Laboratory Medicine, Institution of Microbiology and Infectious Diseases, The First Affiliated Hospital, Hengyang Medical School, Hengyang, China
• ⁴Zhejiang Normal University, Jinhua, China
• ⁵State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau, China

With the development of artificial intelligence(AI) in computer science and statistics, it has been further applied to the medical field. These applications include the management of infectious diseases, in which machine learning has created inroads in clinical microbiology, radiology, genomics, and the analysis of electronic health record data. Especially, the role of machine learning in microbiology has gradually become prominent, and it is used in etiological diagnosis, prediction of antibiotic resistance, association between human microbiome characteristics and complex host diseases, prognosis judgment, and prevention and control of infectious diseases.Machine learning in the field of microbiology mainly adopts supervised learning and unsupervised learning, involving algorithms from classification and regression to clustering and dimensionality reduction. This Review explains crucial concepts in machine learning for unfamiliar readers, describes machine learning's current applications in clinical microbiology and infectious diseases, and summarizes important approaches clinicians must be aware of when evaluating research using machine learning.