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

Angie Li   1 , MD ;   Sarah Mullin   1 , PhD ;   Peter L Elkin   1 , MD

1 Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, United States

Corresponding Author:

  • Angie Li, MD
  • Department of Biomedical Informatics
  • Jacobs School of Medicine and Biomedical Sciences
  • University at Buffalo
  • 77 Goodell Street
  • Suite 540
  • Buffalo, NY, 14203
  • United States
  • Phone: 1 716 888 4858
  • Email: ali83@buffalo.edu