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

Reviews in Medical Informatics (93) Decision Support for Health Professionals (1185) Secondary Use of Clinical Data for Research and Surveillance (366) New Methods (944) Electronic Health Records (1053) Machine Learning (1465)
Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/33875, first published .
Machine Learning Approach for Preterm Birth Prediction Using Health Records: Systematic Review

Machine Learning Approach for Preterm Birth Prediction Using Health Records: Systematic Review

Machine Learning Approach for Preterm Birth Prediction Using Health Records: Systematic Review

Authors of this article:

Zahra Sharifi-Heris1 Author Orcid Image ;   Juho Laitala2 Author Orcid Image ;   Antti Airola2 Author Orcid Image ;   Amir M Rahmani1 Author Orcid Image ;   Miriam Bender1 Author Orcid Image
Article Authors Cited by (13) Tweetations (8) Metrics

Journals

  1. Tricco A, Hezam A, Parker A, Nincic V, Harris C, Fennelly O, Thomas S, Ghassemi M, McGowan J, Paprica P, Straus S. Implemented machine learning tools to inform decision-making for patient care in hospital settings: a scoping review. BMJ Open 2023;13(2):e065845 View
  2. Gubina D, Prokopenko E, Nikol'skaya I. Chronic glomerulonephritis and pregnancy: predictors of preterm birth. Almanac of Clinical Medicine 2023;50(8):463 View
  3. S. H, V. M. An idiosyncratic MIMBO-NBRF based automated system for child birth mode prediction. Artificial Intelligence in Medicine 2023;143:102621 View
  4. Zhang Y, Du S, Hu T, Xu S, Lu H, Xu C, Li J, Zhu X. Establishment of a model for predicting preterm birth based on the machine learning algorithm. BMC Pregnancy and Childbirth 2023;23(1) View
  5. Edwards T, Greene C, Piekos J, Hellwege J, Hampton G, Jasper E, Velez Edwards D. Challenges and Opportunities for Data Science in Women's Health. Annual Review of Biomedical Data Science 2023;6(1):23 View
  6. Raghavan D, Adithya H, Raghuram S, Suma K, Kulhalli T. ANALYSIS OF ELECTROHYSTEROGRAM SIGNALS AND PREDICTION OF PRETERM BIRTHS USING MACHINE LEARNING. Biomedical Engineering: Applications, Basis and Communications 2023;35(06) View
  7. 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
  8. Li H, Gao L, Yang X, Chen L. Development and validation of a risk prediction model for preterm birth in women with gestational diabetes mellitus. Clinical Endocrinology 2024;101(3):206 View
  9. Jeyananthan P, Piyasamara G, Sachintha D. Methylation Data of Parents in the Prediction of a Preterm Birth: A Machine Learning Approach. SN Computer Science 2024;5(5) View
  10. 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
  11. Olapojoye A, Singh A, Nishi E, Fei B, Nostratinia A, Hassanipour F. Infants Sucking Pattern Identification Using Machine-Learned Computational Modeling. Journal of Engineering and Science in Medical Diagnostics and Therapy 2025;8(3) View
  12. Park J, Lee K, Heo J, Ahn K. Clinical and dental predictors of preterm birth using machine learning methods: the MOHEPI study. Scientific Reports 2024;14(1) View