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

Area of Science:

• Reproductive Medicine
• Rheumatology
• Data Science

Background:

• Autoimmune rheumatic diseases affect multiple systems, frequently in women of childbearing age.
• Pregnancy in these patients poses significant risks to maternal-fetal health and outcomes.
• Big data integration in healthcare is driving the use of machine learning (ML) for clinical data analysis.

Purpose of the Study:

• To review the basics of ML and its recent advances in predicting pregnancy complications in autoimmune rheumatic diseases.
• To discuss current trends and challenges in applying ML for clinical decision-making.
• To provide insights for enhancing maternal and child health through ML-assisted management.

Main Methods:

• Literature review of machine learning applications in pregnancy complications associated with autoimmune rheumatic diseases.
• Analysis of current trends and prediction models.
• Discussion of challenges and future research directions.

Main Results:

• ML is increasingly used to mine clinical data for predicting pregnancy complications.
• Various ML applications show promise in identifying risks associated with autoimmune rheumatic diseases during pregnancy.
• Challenges remain in enhancing ML model accuracy, reliability, and clinical applicability.

Conclusions:

• ML offers valuable insights for identifying and managing pregnancy complications in autoimmune rheumatic diseases.
• Further development is needed to improve ML's clinical utility and support comprehensive maternal-fetal care.
• This review establishes a foundation for future research in ML-driven obstetric care for this patient group.