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

Machine Learning (1385) Decision Support for Health Professionals (1137) Electronic Health Records (966) Secondary Use of Clinical Data for Research and Surveillance (320)

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 (5) Tweetations (3) Metrics

Journals

  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