Machine Learning and Precision Medicine in Emergency Medicine: The Basics.
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As machine learning (ML) and precision medicine become more readily available and used in practice, emergency physicians must understand the potential advantages and limitations of the technology. This narrative review focuses on the key components of machine learning, artificial intelligence, and precision medicine in emergency medicine (EM). Based on the content expertise, we identified articles from EM literature. The authors provided a narrative summary of each piece of literature. Next, the authors provided an introduction of the concepts of ML, artificial intelligence as an extension of ML, and precision medicine. This was followed by concrete examples of their applications in practice and research. Subsequently, we shared our thoughts on how to consume the existing research in these subjects and conduct high-quality research for academic emergency medicine. We foresee that the EM community will continue to adapt machine learning, artificial intelligence, and precision medicine in research and practice. We described several key components using our expertise.
research in emergency medicine
Published Version (Please cite this version)10.7759/cureus.17636
Publication InfoLee, Sangil; Lam, Samuel H; Hernandes Rocha, Thiago Augusto; Fleischman, Ross J; Staton, Catherine A; Taylor, Richard; & Limkakeng, Alexander T (2021). Machine Learning and Precision Medicine in Emergency Medicine: The Basics. Cureus, 13(9). pp. e17636. 10.7759/cureus.17636. Retrieved from https://hdl.handle.net/10161/24039.
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Alexander Tan Limkakeng Jr.
Professor of Emergency Medicine
My personal research interest is finding new ways to diagnose acute coronary syndrome. In particular, I am interested in novel biomarkers and precision medicine approaches to this problem. I also have an interest in sepsis and empirical bioethics. As Vice Chair of Clinical Research for the Duke University Department of Emergency Medicine, I also work with researchers from many fields spanning global health, innovation, clinical trials, basic discovery, and trans
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