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Machine Learning (2218) Clinical Informatics (1426) Electronic Health Records (1371) Big Data (317) Secondary Use of Clinical Data for Research and Surveillance (490) Health Care Quality and Health Services Research (359) Clinical Information and Decision Making (2054) Data Science (383)

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 (7) 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
  4. Roebuck C, Sandall J, West R, Atherden C, Parkyn K, Johnson O. Impact of midwife continuity of carer on stillbirth rate and first feed in England. Communications Medicine 2025;5(1) View
  5. Ratchatorn A, Ketdao N, Sonsilphong S, Triamwichanon D, Panitchote A. Deep learning approaches for time series prediction of renal recovery in medical critically Ill patients with acute kidney injury: LSTM, GRU, and transformer models. Critical Care 2026 View
  6. Gazi A, Gao D, Ghosh S, Xu Z, Trella A, Klasnja P, Murphy S. Digital Twins for Just-in-Time Adaptive Interventions (JITAIs): Framework for Optimizing and Continually Improving JITAIs. Journal of Medical Internet Research 2026;28:e72830 View

Conference Proceedings

  1. Fan X. 2025 International Joint Conference on Neural Networks (IJCNN). Position Paper: Integrating Explainability and Uncertainty Estimation in Medical AI View