TY - GEN
T1 - Bibliometric Analysis of Intelligent Techniques for Obstetric Complication Prediction in the Last 20 Years
AU - Lestari, Dian
AU - Maulana, Fairuz Iqbal
AU - Adi, Puput Dani Prasetyo
AU - Rahayu, Anita
AU - Nadlifatin, Reny
AU - Widartha, Vandha Pradwiyasma
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - bibliometric
KW - machine learning
KW - obstetric complication
UR - http://www.scopus.com/inward/record.url?scp=85203693033&partnerID=8YFLogxK
U2 - 10.1109/eSmarTA62850.2024.10638994
DO - 10.1109/eSmarTA62850.2024.10638994
M3 - Conference contribution
AN - SCOPUS:85203693033
T3 - 4th International Conference on Emerging Smart Technologies and Applications, eSmarTA 2024
BT - 4th International Conference on Emerging Smart Technologies and Applications, eSmarTA 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on Emerging Smart Technologies and Applications, eSmarTA 2024
Y2 - 6 August 2024 through 7 August 2024
ER -
