Classifying Pseudogout Using Machine Learning Approaches With Electronic Health Record Data.

Journal: Arthritis care & research
PMID: 31910317

Abstract

OBJECTIVE: Identifying pseudogout in large data sets is difficult due to its episodic nature and a lack of billing codes specific to this acute subtype of calcium pyrophosphate (CPP) deposition disease. The objective of this study was to evaluate a novel machine learning approach for classifying pseudogout using electronic health record (EHR) data.

Authors

  • Sara K Tedeschi
    Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
  • Tianrun Cai
    Harvard-MIT Center for Regulatory Science, Harvard Medical School, Boston, MA, United States.
  • Zeling He
    Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
  • Yuri Ahuja
    Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
  • Chuan Hong
    Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
  • Katherine A Yates
    Harvard Medical School, Boston, Massachusetts.
  • Kumar Dahal
    Division of Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
  • Chang Xu
    Institute of Cardio-Cerebrovascular Medicine, Central Hospital of Dalian University of Technology, Dalian 116089, China.
  • Houchen Lyu
    Brigham and Women's Hospital, Boston, Massachusetts.
  • Kazuki Yoshida
    Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
  • Daniel H Solomon
    Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital.
  • Tianxi Cai
    Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States.
  • Katherine P Liao
    Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.

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