This paper is in the following e-collection/theme issue:

Machine Learning (1927) Decision Support for Health Professionals (1447) Electronic Health Records (1254) Secondary Use of Clinical Data for Research and Surveillance (447)

Published on in Vol 10, No 6 (2022): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/33835, first published .
The Prediction of Preterm Birth Using Time-Series Technology-Based Machine Learning: Retrospective Cohort Study

The Prediction of Preterm Birth Using Time-Series Technology-Based Machine Learning: Retrospective Cohort Study

The Prediction of Preterm Birth Using Time-Series Technology-Based Machine Learning: Retrospective Cohort Study

Authors of this article:

Yichao Zhang1 Author Orcid Image ;   Sha Lu2, 3 Author Orcid Image ;   Yina Wu1 Author Orcid Image ;   Wensheng Hu2, 3 Author Orcid Image ;   Zhenming Yuan1 Author Orcid Image
Article Authors Cited by (11) Tweetations (3) Metrics

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  1. Mennickent D, Rodríguez A, Opazo M, Riedel C, Castro E, Eriz-Salinas A, Appel-Rubio J, Aguayo C, Damiano A, Guzmán-Gutiérrez E, Araya J. Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications. Frontiers in Endocrinology 2023;14 View
  2. Medjedovic E, Stanojevic M, Jonuzovic-Prosic S, Ribic E, Begic Z, Cerovac A, Badnjevic A. Artificial intelligence as a new answer to old challenges in maternal-fetal medicine and obstetrics. Technology and Health Care 2024;32(3):1273 View
  3. Yu Q, Lin Y, Zhou Y, Yang X, Hemelaar J. Predicting risk of preterm birth in singleton pregnancies using machine learning algorithms. Frontiers in Big Data 2024;7 View
  4. Feli M, Azimi I, Sarhaddi F, Sharifi-Heris Z, Niela-Vilen H, Liljeberg P, Axelin A, Rahmani A. Preterm birth risk stratification through longitudinal heart rate and HRV monitoring in daily life. Scientific Reports 2024;14(1) View
  5. Abdurrahman E, Siregar K, Rikawarastuti , Sutedja I, Nasir N. Machine Learning-based Prediction Model for Adverse Pregnancy Outcomes: A Systematic Literature Review. JURNAL INFO KESEHATAN 2024;22(3):532 View
  6. Dakheel A, Mohammed M, Alhuseen Z, Hashim W. Deep Learning-Based Decision Support System for Predicting Pregnancy Risk Levels through Cardiotocograph (CTG) Imaging Analysis. Intelligent Automation & Soft Computing 2025;40(1):195 View
  7. Xu Y, Zu Y, Zhang Y, Liang Z, Xu X, Yan J. A Multi-Algorithm Machine Learning Model for Predicting the Risk of Preterm Birth in Patients with Early-Onset Preeclampsia. International Journal of General Medicine 2025;Volume 18:4195 View
  8. Vasudevan L, Kibria M, Kucirka L, Shieh K, Wei M, Masoumi S, Balasubramanian S, Victor A, Conklin J, Gurcan M, Stuebe A, Page D. Machine Learning Models to Predict Risk of Maternal Morbidity and Mortality From Electronic Medical Record Data: Scoping Review. Journal of Medical Internet Research 2025;27:e68225 View

Books/Policy Documents

  1. Sharma P, Gupta T, Kulshrestha S, Gupta P, Choudhury A, Modi D, Sengupta A. Data-Driven Reproductive Health. View

Conference Proceedings

  1. Damaraji G, Permanasari A, Hidayah I, Moses Paknahan M, Wardhana A. 2022 4th International Conference on Biomedical Engineering (IBIOMED). Detecting Pregnancy Risk Type Using LSTM Algorithm View
  2. De Sousa Vitória A, Mori A, Silva D, Do Prado Pagotto D, Coelho C, Filho A. 2023 IEEE 11th International Conference on Healthcare Informatics (ICHI). Live Births Forecasting Across Health Regions of Goiás Using Artificial Neural Networks: A Clustering Approach View