A machine learning-based diagnosis modeling of IgG4 Hashimoto's thyroiditis.

Journal: Endocrine
PMID:

Abstract

PURPOSE: This study aims to develop a non-invasive diagnosis model using machine learning (ML) for identifying high-risk IgG4 Hashimoto's thyroiditis (HT) patients.

Authors

  • Chenxu Zhao
    Department of Endocrinology, Peking University First Hospital, 100034, Beijing, China.
  • Zhiming Sun
    Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin, China.
  • Yang Yu
    Division of Cardiology, the Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Yiwei Lou
    School of Computer Science, Peking University, 100871, Beijing, China.
  • Liyuan Liu
    State Key Laboratory of Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China.
  • Ge Li
    Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510100, China.
  • Jumei Liu
    Department of Pathology, Peking University First Hospital, 100034, Beijing, China.
  • Lei Chen
    Department of Chemistry, Stony Brook University Stony Brook NY USA.
  • Sainan Zhu
    Department of Statistics, The First Hospital of Peking University Beijing 100030, China.
  • Yu Huang
    School of Data Science and Software Engineering, Qingdao University, Qingdao 266021, China.
  • Yang Zhang
    Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
  • Ying Gao
    Department of Pharmacy The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology Kunming China.