Influence of medical domain knowledge on deep learning for Alzheimer's disease prediction.

Journal: Computer methods and programs in biomedicine
Published Date: Sep 20, 2020

Abstract

BACKGROUND AND OBJECTIVE: Alzheimer's disease (AD) is the most common type of dementia that can seriously affect a person's ability to perform daily activities. Estimates indicate that AD may rank third as a cause of death for older people, after heart disease and cancer. Identification of individuals at risk for developing AD is imperative for testing therapeutic interventions. The objective of the study was to determine could diagnostics of AD from EMR data alone (without relying on diagnostic imaging) be significantly improved by applying clinical domain knowledge in data preprocessing and positive dataset selection rather than setting naïve filters.

Authors

  • Branimir Ljubic
    Center for Data Analytics and Biomedical Informatics, Temple University, Philadelphia, Pennsylvania, USA.
  • Shoumik Roychoudhury
    Center for Data Analytics and Biomedical Informatics (DABI), Temple University, 1925 N 12th Street, SERC 035-02, Philadelphia, PA 19122, USA.
  • Xi Hang Cao
    Center for Data Analytics and Biomedical Informatics (DABI), Temple University, 1925 N 12th Street, SERC 035-02, Philadelphia, PA 19122, USA.
  • Martin Pavlovski
    Macedonian Academy of Sciences and Arts, Research Center for Computer Science and Information Technologies, Skopje, 1000, Republic of Macedonia.
  • Stefan Obradovic
    Department of Computer Science, Brendan Iribe Center for Computer Science and Engineering, University of Maryland, 8125 Paint Branch Drive, College Park, MD 20742, USA.
  • Richard Nair
    IQVIA, Plymouth Meeting, PA 19462, USA.
  • Lucas Glass
    IQVIA, Plymouth Meeting, Pennsylvania, USA.
  • Zoran Obradovic

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