Automated detection of altered mental status in emergency department clinical notes: a deep learning approach.

Journal: BMC medical informatics and decision making
Published Date: Aug 19, 2019

Abstract

BACKGROUND: Machine learning has been used extensively in clinical text classification tasks. Deep learning approaches using word embeddings have been recently gaining momentum in biomedical applications. In an effort to automate the identification of altered mental status (AMS) in emergency department provider notes for the purpose of decision support, we compare the performance of classic bag-of-words-based machine learning classifiers and novel deep learning approaches.

Authors

  • Jihad S Obeid
    Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC 29425, United States.
  • Erin R Weeda
    Department of Clinical Pharmacy and Outcome Sciences, Medical University of South Carolina, Charleston, SC, USA.
  • Andrew J Matuskowitz
    Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Dr, Charleston, SC 29425-2260 (S.S.M., D.M., M.v.A., C.N.D.C., R.R.B., C.T., A.V.S., A.M.F., B.E.J., L.P.G., U.J.S.); Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany (S.S.M., T.J.V.); Stanford University School of Medicine, Department of Radiology, Stanford, Calif (D.M.); Division of Cardiothoracic Imaging, Nuclear Medicine and Molecular Imaging, Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (C.N.D.C.); Division of Cardiology, Department of Medicine, Medical University of South Carolina, Charleston, SC (R.R.B.); Department of Cardiology and Intensive Care Medicine, Heart Center Munich-Bogenhausen, Munich, Germany (C.T.); Department of Cardiology, Munich University Clinic, Ludwig-Maximilians-University, Munich, Germany (C.T.); Siemens Medical Solutions USA, Malvern, Pa (P.S.); and Department of Emergency Medicine, Medical University of South Carolina, Charleston, SC (A.J.M.).
  • Kevin Gagnon
    Department of Computer Science, University of South Carolina, Columbia, SC, USA.
  • Tami Crawford
    Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC, USA.
  • Christine M Carr
    Department of Emergency Medicine, Medical University of South Carolina, Charleston, SC, USA.
  • Lewis J Frey
    Ralph H. Johnson Veterans Affairs Medical Center, Charleston, South Carolina, USA.

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