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

Artificial Intelligence (1189) Decision Support for Health Professionals (1129) Machine Learning (1373) Alcohol Abuse, Alcoholism Prevention and Recovery (283)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/45041, first published .
Predicting Fetal Alcohol Spectrum Disorders Using Machine Learning Techniques: Multisite Retrospective Cohort Study

Predicting Fetal Alcohol Spectrum Disorders Using Machine Learning Techniques: Multisite Retrospective Cohort Study

Predicting Fetal Alcohol Spectrum Disorders Using Machine Learning Techniques: Multisite Retrospective Cohort Study

Authors of this article:

Sarah Soyeon Oh1, 2 Author Orcid Image ;   Irene Kuang3 Author Orcid Image ;   Hyewon Jeong3 Author Orcid Image ;   Jin-Yeop Song4 Author Orcid Image ;   Boyu Ren5 Author Orcid Image ;   Jong Youn Moon6 Author Orcid Image ;   Eun-Cheol Park2 Author Orcid Image ;   Ichiro Kawachi1 Author Orcid Image
Article Authors Cited by (2) Tweetations (4) Metrics

Journals

  1. Bandoli G, Coles C, Kable J, Jones K, Wertelecki W, Yevtushok L, Zymak‐Zakutnya N, Granovska I, Plotka L, Chambers C. Predicting fetal alcohol spectrum disorders in preschool‐aged children from early life factors. Alcohol: Clinical and Experimental Research 2024;48(1):122 View
  2. Ramos-Triguero A, Navarro-Tapia E, Vieiros M, Mirahi A, Astals Vizcaino M, Almela L, Martínez L, García-Algar Ó, Andreu-Fernández V. Machine learning algorithms to the early diagnosis of fetal alcohol spectrum disorders. Frontiers in Neuroscience 2024;18 View