Machine learning techniques for prediction in pregnancy complicated by autoimmune rheumatic diseases: Applications and challenges.

Journal: International immunopharmacology
PMID: 38735259

Abstract

Autoimmune rheumatic diseases are chronic conditions affecting multiple systems and often occurring in young women of childbearing age. The diseases and the physiological characteristics of pregnancy significantly impact maternal-fetal health and pregnancy outcomes. Currently, the integration of big data with healthcare has led to the increasing popularity of using machine learning (ML) to mine clinical data for studying pregnancy complications. In this review, we introduce the basics of ML and the recent advances and trends of ML in different prediction applications for common pregnancy complications by autoimmune rheumatic diseases. Finally, the challenges and future for enhancing the accuracy, reliability, and clinical applicability of ML in prediction have been discussed. This review will provide insights into the utilization of ML in identifying and assisting clinical decision-making for pregnancy complications, while also establishing a foundation for exploring comprehensive management strategies for pregnancy and enhancing maternal and child health.

Authors

  • Xiaoshi Zhou
    Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
  • Feifei Cai
    Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
  • Shiran Li
    Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
  • Guolin Li
    Intelligent Manufacturing Research Center, Guangdong Midea Air-Conditioning Equipment Co., Ltd, Foshan, China.
  • Changji Zhang
    Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China; School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China.
  • Jingxian Xie
    Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China; College of Pharmacy, Southwest Medical University, Luzhou, China.
  • Yong Yang
    Department of Radiation Oncology, Stanford University, CA, USA.

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