Levy et al. Alzheimers Dis Dement 2017, 1(2):38-46 *Corresponding authors: Boaz Levy, Department of Psy- chiatry, McLean Hospital, Harvard Medical School, COP 110, 115 Mill St. Belmont, MA 02478; Department of Coun- seling and School Psychology, University of Massachusetts, Boston, 100 Morrissey Blvd, Boston 02125, USA, Tel: 617- 3090318, E-mail: boaz.levy@umb.edu Received: July 12, 2017; Accepted: September 12, 2017; Published online: September 14, 2017 Citation: Levy B, Gable S, Tsoy E, et al. (2017) Machine Learning Detection of Cognitive Impairment in Primary Care. Alzheimers Dis Dement 1(2):38-46 Copyright: © 2017 Levy B, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Short Commentary Open Access Alzheimer's Disease & Dementia • Page 38 • ISSN: 2578-6490 | DOI: 10.36959/734/372 | Volume 1 | Issue 2 Machine Learning Detection of Cognitive Impairment in Pri- mary Care Boaz Levy 1 *, Samuel Gable 1 , Elena Tsoy 1 , Nurit Haspel 1 , Brianna Wadler 1 , Rand Wilcox 2 , Courtney Hess 1 , Jacqueline Hogan 1 , Daniel Driscoll 3 and Ardeshir Hashmi 4 1 Department of Counseling and School Psychology, University of Massachusetts, USA 2 Department of Psychology, University of Southern California, USA 3 Tufts University School of Medicine, USA 4 Massachusetts General Hospital, Harvard Medical School, USA Abstract Purpose: Routine cognitive screenings in primary care settings can beneft patient care and preventive medicine in multiple ways; however, their integration to the protocol of physical exams, as a standard of care, may be hampered by systemic considerations related to labor and cost. In an efort to decrease these impediments, the current study evaluated the validity of a screening procedure that had been specifcally designed to impose minimal burden on the clinic. Method: Te study examined the ability of a brief computerized cognitive test (the CNS Screen) to detect mild cognitive impairment in a cross-sectional design. Analyses employed a machine learning model of Support Vector Machines (SVM) to classify non-symptomatic subjects from a primary care clinic (n = 49) and hospitalized psychiatric patients with mild cognitive impairment (n = 26), based on the screening data. Results: Te classifying algorithm correctly assigned participants to their respective groups at a probability of 0.945 through a ‘leave one out’ validation procedure. Conclusions: Tese fndings suggest that instruments such as the CNS Screen may ofer a pragmatic alternative to clinician-administered procedures, while maintaining the validity required for clinical practice. Implications for patient care in primary care settings are discussed. Keywords Neuropsychological testing, Cognitive impairment, Primary care, Prevention, Support vector machine learning Abbreviations MMSE: Mini-Mental State Examination; MoCA: Montreal Cognitive Assessment; PHQ-4: Patient Health Questionnaire-4; QIDS-SR16: Quick Inventory of Depressive Symptomatology (self-rated); HAM-D: Hamilton Depression Rating Scale; IDS-SR: Inventory of Depressive Symptomatology (self-report) Introduction Although central to most aspects of human life, the brain receives relatively limited attention during phys- ical exams. At current, the routine monitoring of brain health is excluded from primary care practices primarily due to practical considerations. In particular, biological- ly based procedures that can reveal brain pathology are too invasive, labor-intensive, and expensive to employ in community wide screenings [1]. In light of such prohib- itive burden, assessing brain functioning through a cog-