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

Machine Learning (1501) Decision Support for Health Professionals (1210) Advanced Data Analytics in eHealth (142) Tools, Programs and Algorithms (275) Clinical Informatics (1050) Clinical Information and Decision Making (1434) Pediatrics (205)

Published on in Vol 12 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42271, first published .
Improving Prediction of Survival for Extremely Premature Infants Born at 23 to 29 Weeks Gestational Age in the Neonatal Intensive Care Unit: Development and Evaluation of Machine Learning Models

Improving Prediction of Survival for Extremely Premature Infants Born at 23 to 29 Weeks Gestational Age in the Neonatal Intensive Care Unit: Development and Evaluation of Machine Learning Models

Improving Prediction of Survival for Extremely Premature Infants Born at 23 to 29 Weeks Gestational Age in the Neonatal Intensive Care Unit: Development and Evaluation of Machine Learning Models

Authors of this article:

Angie Li1 Author Orcid Image ;   Sarah Mullin1 Author Orcid Image ;   Peter L Elkin1 Author Orcid Image
Article Authors Cited by (2) Tweetations (3) Metrics

Journals

  1. Franklin G, Stephens R, Piracha M, Tiosano S, Lehouillier F, Koppel R, Elkin P. The Sociodemographic Biases in Machine Learning Algorithms: A Biomedical Informatics Perspective. Life 2024;14(6):652 View
  2. Biniwale M. Neuromuscular components of Apgar score in predicting delivery room respiratory support. Pediatric Research 2024;96(4):834 View