Evolving Applications of Artificial Intelligence and Machine Learning in Infectious Diseases Testing

作者：Nam K. Tran, Samer Albahra, Larissa May, Sarah Waldman, Scott J. Crabtree, Scott Bainbridge, Hooman H. Rashidi · 发表于：Clinical Chemistry · 年份：2021 · DOI：10.1093/clinchem/hvab239 · 被引用次数：73 · 研究领域：Artificial Intelligence in Healthcare and Education、Bacterial Identification and Susceptibility Testing、Clinical Laboratory Practices and Quality Control

BACKGROUND: Artificial intelligence (AI) and machine learning (ML) are poised to transform infectious disease testing. Uniquely, infectious disease testing is technologically diverse spaces in laboratory medicine, where multiple platforms and approaches may be required to support clinical decision-making. Despite advances in laboratory informatics, the vast array of infectious disease data is constrained by human analytical limitations. Machine learning can exploit multiple data streams, including but not limited to laboratory information and overcome human limitations to provide physicians with predictive and actionable results. As a quickly evolving area of computer science, laboratory professionals should become aware of AI/ML applications for infectious disease testing as more platforms are become commercially available. CONTENT: In this review we: (a) define both AI/ML, (b) provide an overview of common ML approaches used in laboratory medicine, (c) describe the current AI/ML landscape as it relates infectious disease testing, and (d) discuss the future evolution AI/ML for infectious disease testing in both laboratory and point-of-care applications. SUMMARY: The review provides an important educational overview of AI/ML technique in the context of infectious disease testing. This includes supervised ML approaches, which are frequently used in laboratory medicine applications including infectious diseases, such as COVID-19, sepsis, hepatitis, malaria, meningitis, Lyme dis...