Mini-mental status examination phenotyping for Alzheimer's disease patients using both structured and narrative electronic health record features.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 39520712

Abstract

OBJECTIVE: This study aims to automate the prediction of Mini-Mental State Examination (MMSE) scores, a widely adopted standard for cognitive assessment in patients with Alzheimer's disease, using natural language processing (NLP) and machine learning (ML) on structured and unstructured EHR data.

Authors

  • Betina Idnay
    School of Nursing, Columbia University, New York, New York, USA.
  • Gongbo Zhang
    Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, United States.
  • Fangyi Chen
    Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, United States.
  • Casey N Ta
    Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA.
  • Matthew W Schelke
    Department of Neurology, Columbia University Irving Medical Center, New York, NY 10032, United States.
  • Karen Marder
    Columbia University, Department of Neurology, New York, NY, USA.
  • Chunhua Weng
    Department of Biomedical Informatics, Columbia University.

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