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

Machine Learning (1694) Clinical Informatics (1142) Electronic Health Records (1163) Big Data (277) Secondary Use of Clinical Data for Research and Surveillance (412) Health Care Quality and Health Services Research (284) Clinical Information and Decision Making (1612) Data Science (300)

Published on in Vol 9, No 12 (2021): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25022, first published .
On Missingness Features in Machine Learning Models for Critical Care: Observational Study

On Missingness Features in Machine Learning Models for Critical Care: Observational Study

On Missingness Features in Machine Learning Models for Critical Care: Observational Study

Authors of this article:

Janmajay Singh1 Author Orcid Image ;   Masahiro Sato1 Author Orcid Image ;   Tomoko Ohkuma1 Author Orcid Image
Article Authors Cited by (3) Tweetations (3) Metrics

Journals

  1. Srimedha B, Naveen Raj R, Mayya V. A Comprehensive Machine Learning Based Pipeline for an Accurate Early Prediction of Sepsis in ICU. IEEE Access 2022;10:105120 View
  2. Ranard B, Park S, Jia Y, Zhang Y, Alwan F, Celi L, Lusczek E. Minimizing bias when using artificial intelligence in critical care medicine. Journal of Critical Care 2024;82:154796 View
  3. Velez T, Ibrahim Z, Duru K, Velez D, Triantafyllou M, McKinley K, Saif P, Kratimenos P, Clark A, Koutroulis I. Predicting hospital admissions, ICU utilization, and prolonged length of stay among febrile pediatric emergency department patients using incomplete and imbalanced electronic health record (EHR) data strategies. International Journal of Medical Informatics 2025;200:105905 View