Bibliometric Analysis of Intelligent Techniques for Obstetric Complication Prediction in the Last 20 Years

This study conducts a bibliometric analysis of intelligent techniques in predicting obstetric complications over the past 20 years. These complications significantly impact maternal and neonatal health, necessitating early prediction and management. The study highlights the growing use of machine learning (ML) and artificial intelligence (AI) in this field. By reviewing literature from 2004 to 2023, it identifies a gap in research focused on using intelligent techniques for predicting obstetric complications. Keyword co-occurrence analysis reveals significant research clusters in areas like diabetes mellitus, biomarkers, and treatment management. The study emphasizes the need for practical ML-based applications to aid clinical decision-making and suggests that trend data should inform curriculum development and clinical practice guidelines in obstetrics.