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

Reviews in Medical Informatics (93) Decision Support for Health Professionals (1210) Secondary Use of Clinical Data for Research and Surveillance (375) New Methods (963) Electronic Health Records (1068) Machine Learning (1501)
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

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  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
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