Quantifying the Impacts of Pre- and Post-Conception TSH Levels on Birth Outcomes: An Examination of Different Machine Learning Models.

Journal: Frontiers in endocrinology
PMID: 34777251

Abstract

BACKGROUND: While previous studies identified risk factors for diverse pregnancy outcomes, traditional statistical methods had limited ability to quantify their impacts on birth outcomes precisely. We aimed to use a novel approach that applied different machine learning models to not only predict birth outcomes but systematically quantify the impacts of pre- and post-conception serum thyroid-stimulating hormone (TSH) levels and other predictive characteristics on birth outcomes.

Authors

  • Yuantong Sun
    Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, United States.
  • Weiwei Zheng
    Key Laboratory of the Public Health Safety, Ministry of Education, Department of Environmental Health, School of Public Health, Fudan University, Shanghai, China.
  • Ling Zhang
  • Huijuan Zhao
    Key Laboratory of the Public Health Safety, Ministry of Education, Department of Environmental Health, School of Public Health, Fudan University, Shanghai, China.
  • Xun Li
    Department of Laboratory Medicine, The First Affiliated Hospital of Xiamen University, Xiamen, China.
  • Chao Zhang
    School of Information Engineering, Suqian University, Suqian, Jiangsu, China.
  • Wuren Ma
    Key Laboratory of the Public Health Safety, Ministry of Education, Department of Environmental Health, School of Public Health, Fudan University, Shanghai, China.
  • Dajun Tian
    Department of Epidemiology and Biostatistics, College for Public Health and Social Justice, Saint Louis University, St. Louis, MO, United States.
  • Kun-Hsing Yu
    Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
  • Shuo Xiao
    Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Environmental and Occupational Health Sciences Institute, Rutgers University, Piscataway, NJ, United States.
  • Liping Jin
    Department of Maternity and Children's Health Care Department, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, China.
  • Jing Hua
    School of Software, Jiangxi Agricultural University, Nanchang 330045, China.

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