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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Quality improvement in neurology: dementia management quality measures

Germaine Odenheimer1, Soo Borson, Amy E Sanders

  • 1From the Department of Geriatric Medicine (G.O.), University of Oklahoma College of Medicine, Oklahoma City; Veterans Affairs Medical Center (G.O.), Oklahoma City, OK; Department of Psychiatry and Behavioral Sciences (S.B.), University of Washington, School of Medicine, Seattle; Department of Neurology (A.E.S.), Albert Einstein College of Medicine, Bronx, NY; American Academy of Neurology (R.J.S.-E.), Minneapolis, MN; Department of Psychiatry (H.H.K.), McLean Hospital and Harvard Medical School, Boston, MA; American Medical Association (S.T.), Chicago, IL; Center for Innovative Care in Aging (L.N.G.), Community Public Health, School of Nursing and Department of Psychiatry, Division of Geriatrics and Gerontology, Johns Hopkins University, School of Medicine, Baltimore, MD; Division of Geriatric Medicine (M.A.F., J.J.), University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA; Absher Neurology (J.A.), Greenville, SC; and Department of Geriatrics and Palliative Medicine (J.S.), University of Chicago, IL.

Neurology
|September 27, 2013
PubMed
Abstract

No abstract available in PubMed .

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