传染病(医学专业)
人工智能
机器学习
检测点注意事项
计算机科学
疾病
医学实验室
背景(考古学)
人工智能应用
数据科学
医学
病理
生物
古生物学
作者
Nam K. Tran,Samer Albahra,Larissa May,Sarah Waldman,Scott Crabtree,Scott Bainbridge,Hooman H. Rashidi
出处
期刊:Clinical Chemistry
[Oxford University Press]
日期:2021-12-30
卷期号:68 (1): 125-133
被引量:26
标识
DOI:10.1093/clinchem/hvab239
摘要
Abstract 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 disease, and tuberculosis. We also apply the concept of “data fusion” describing the future of laboratory testing where multiple data streams are integrated by AI/ML to provide actionable clinical knowledge.
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