Machine Learning for Clinical Chemists
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Author(s)
Banfi, Giuseppe
Bietenbeck, Andreas
Cervinski, Mark A
Loh, Tze Ping
Sikaris, Ken
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Abstract
Medicine has traditionally relied on heuristic approaches in which knowledge is acquired through experience and self-learning. Pathology is information rich with quantitative and qualitative measurements such as history, images, and physiological data from which diagnosis and treatment decisions are made. This information is readily linked to patient outcome data and is therefore potentially invaluable in improving treatment. Thus, pathology is ripe for the application of tools that can effectively turn this information into wisdom.
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Clinical Chemistry
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65
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11
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Self-archiving of the author-manuscript version is not yet supported by this journal. Please refer to the journal link for access to the definitive, published version or contact the author[s] for more information.
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Subject
Medical biotechnology
Medical biochemistry and metabolomics
Clinical sciences
Medical Laboratory Technology
Science & Technology
Life Sciences & Biomedicine
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Badrick, T; Banfi, G; Bietenbeck, A; Cervinski, MA; Loh, TP; Sikaris, K, Machine Learning for Clinical Chemists, Clinical Chemistry, 2019, 65 (11), pp. 1350-1356