RegEMR: a natural language processing system to automatically identify premature ovarian decline from Chinese electronic medical records.

Journal: BMC medical informatics and decision making
Published Date: Jul 18, 2023

Abstract

BACKGROUND: The ovarian reserve is a reservoir for reproductive potential. In clinical practice, early detection and treatment of premature ovarian decline characterized by abnormal ovarian reserve tests is regarded as a critical measure to prevent infertility. However, the relevant data are typically stored in an unstructured format in a hospital's electronic medical record (EMR) system, and their retrieval requires tedious manual abstraction by domain experts. Computational tools are therefore needed to reduce the workload.

Authors

  • Jie Cai
    Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou, 510006, China.
  • Shenglin Chen
    Center for Reproductive Medicine, Department of Gynecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
  • Siyun Guo
    Center for Reproductive Medicine, Department of Gynecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
  • Suidong Wang
    Center for Reproductive Medicine, Department of Gynecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
  • Lintong Li
    Center for Reproductive Medicine, Department of Gynecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
  • Xiaotong Liu
    School of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, China.
  • Keming Zheng
    Center for Reproductive Medicine, Department of Gynecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
  • Yudong Liu
  • Shiling Chen
    Center for Reproductive Medicine, Department of Gynecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China. chensl_92@vip.163.com.

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