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

Machine Learning (1370) Hygiene and Infection Prevention (297) Infectious Diseases (non-STD/STI) (1478) Intensive Care Unit (ICU) (101)

Published on in Vol 8, No 7 (2020): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/15965, first published .
A Predictive Model Based on Machine Learning for the Early Detection of Late-Onset Neonatal Sepsis: Development and Observational Study

A Predictive Model Based on Machine Learning for the Early Detection of Late-Onset Neonatal Sepsis: Development and Observational Study

A Predictive Model Based on Machine Learning for the Early Detection of Late-Onset Neonatal Sepsis: Development and Observational Study

Authors of this article:

Wongeun Song1 Author Orcid Image ;   Se Young Jung1 Author Orcid Image ;   Hyunyoung Baek1 Author Orcid Image ;   Chang Won Choi2 Author Orcid Image ;   Young Hwa Jung2 Author Orcid Image ;   Sooyoung Yoo1 Author Orcid Image
Article Authors Cited by (25) Tweetations (1) Metrics

Wongeun Song   1 * , MS ;   Se Young Jung   1 * , MPH, MD ;   Hyunyoung Baek   1 , RN, MPH ;   Chang Won Choi   2 , MD, PhD ;   Young Hwa Jung   2 , MD ;   Sooyoung Yoo   1 , PhD

1 Healthcare ICT Research Center, Office of eHealth Research and Businesses, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea

2 Department of Pediatrics, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea

*these authors contributed equally

Corresponding Author:

  • Sooyoung Yoo, PhD
  • Healthcare ICT Research Center
  • Office of eHealth Research and Businesses
  • Seoul National University Bundang Hospital
  • 172 Dolma-ro, Bundang-gu
  • Seongnam-si, 13620
  • Republic of Korea
  • Phone: 82 32-787-8980
  • Email: yoosoo0@snubh.org