Rare disease knowledge enrichment through a data-driven approach.

Journal: BMC medical informatics and decision making
Published Date: Feb 14, 2019

Abstract

BACKGROUND: Existing resources to assist the diagnosis of rare diseases are usually curated from the literature that can be limited for clinical use. It often takes substantial effort before the suspicion of a rare disease is even raised to utilize those resources. The primary goal of this study was to apply a data-driven approach to enrich existing rare disease resources by mining phenotype-disease associations from electronic medical record (EMR).

Authors

  • Feichen Shen
    Department of Health Sciences Research, Rochester MN.
  • Yiqing Zhao
    Department of Health Informatics and Administration, Center for Biomedical Data and Language Processing, University of Wisconsin-Milwaukee, 2025 E Newport Ave, NWQ-B Room 6469, Milwaukee, WI, 53211, USA.
  • Liwei Wang
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
  • Majid Rastegar Mojarad
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
  • Yanshan Wang
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
  • Sijia Liu
    These authors contributed equally to this study and Dr. Li is now working at IBM; Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA; Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY, USA.
  • Hongfang Liu
    Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, United States.

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