In this issue of JAMA, Seymour and his multidisciplinary team of coauthors1 aim to improve the current understanding of sepsis by identifying new clinical phenotypes using machine learning clustering techniques. The authors assume that current sepsis definitions are too broad and clinically imprecise to untangle complex clinical and biological interactions in sepsis and that better-defined phenotypes represent the key to ultimately identifying new and successful therapeutic approaches. Their analytic approaches, such as unsupervised clustering, may be unfamiliar to many readers. Unsupervised learning is a type of machine learning algorithm used to draw inferences from data sets consisting of input data without labeled responses or direction. The most common unsupervised learning method is a cluster analysis, which is used for exploratory data analysis to find hidden patterns or groupings in the data.
Knaus WA, Marks RD. New Phenotypes for Sepsis: The Promise and Problem of Applying Machine Learning and Artificial Intelligence in Clinical Research. JAMA. 2019;321(20):1981–1982. doi:10.1001/jama.2019.5794
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