Informatics and Statistics

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Machine Learning in Clinical Pathology: Seeing the Forest for the Trees
Ramy Arnaout

Clinical Chemistry Nov 2018, 64 (11) 1553-1554; DOI: 10.1373/clinchem.2018.295121
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Using Machine Learning to Aid the Interpretation of Urine Steroid Profiles
Edmund H. Wilkes, Gill Rumsby, Gary M. Woodward

Clinical Chemistry Nov 2018, 64 (11) 1586-1595; DOI: 10.1373/clinchem.2018.292201
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Starting Is Half
Sterling T. Bennett

Clinical Chemistry Mar 2018, 64 (3) 427-428; DOI: 10.1373/clinchem.2017.284398
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Very Deep Convolutional Neural Networks for Morphologic Classification of Erythrocytes
Thomas J.S. Durant, Eben M. Olson, Wade L. Schulz, Richard Torres

Clinical Chemistry Dec 2017, 63 (12) 1847-1855; DOI: 10.1373/clinchem.2017.276345
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Cerebrospinal Fluid Total Protein Reference Intervals Derived from 20 Years of Patient Data
Christopher R. McCudden, John Brooks, Priya Figurado, Pierre R. Bourque

Clinical Chemistry Dec 2017, 63 (12) 1856-1865; DOI: 10.1373/clinchem.2017.278267
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Deep Learning Makes Its Way to the Clinical Laboratory
Ronald Jackups

Clinical Chemistry Dec 2017, 63 (12) 1790-1791; DOI: 10.1373/clinchem.2017.280768
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Assessing Agreement between Methods of Measurement
Douglas G. Altman, J. Martin Bland

Clinical Chemistry Oct 2017, 63 (10) 1653-1654; DOI: 10.1373/clinchem.2016.268870
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Patient Privacy and Clinical Laboratory Data
Jason Y. Park, Toby C. Cornish, Michael Hogarth, Brian R. Jackson, Kristen B. Rosati

Clinical Chemistry Aug 2017, 63 (8) 1321-1325; DOI: 10.1373/clinchem.2016.266551
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Selecting Statistical Quality Control Procedures for Limiting the Impact of Increases in Analytical Random Error on Patient Safety
Martín Yago

Clinical Chemistry May 2017, 63 (5) 1022-1030; DOI: 10.1373/clinchem.2016.267682
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Patient Consent and the Commercialization of Lab Data
Adam Tanner

Clinical Chemistry Feb 2017, 63 (2) 475-476; DOI: 10.1373/clinchem.2016.261479

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