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

Machine Learning (1829) Big Data (290) Electronic Health Records (1216) Intensive Care Unit (ICU) (143) Clinical Information and Decision Making (1723) Clinical Informatics (1191)

Published on in Vol 9, No 11 (2021): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26426, first published .
Event Prediction Model Considering Time and Input Error Using Electronic Medical Records in the Intensive Care Unit: Retrospective Study

Event Prediction Model Considering Time and Input Error Using Electronic Medical Records in the Intensive Care Unit: Retrospective Study

Event Prediction Model Considering Time and Input Error Using Electronic Medical Records in the Intensive Care Unit: Retrospective Study

Authors of this article:

MinDong Sung1 Author Orcid Image ;   Sangchul Hahn2 Author Orcid Image ;   Chang Hoon Han3 Author Orcid Image ;   Jung Mo Lee3 Author Orcid Image ;   Jayoung Lee2 Author Orcid Image ;   Jinkyu Yoo2 Author Orcid Image ;   Jay Heo4 Author Orcid Image ;   Young Sam Kim5 Author Orcid Image ;   Kyung Soo Chung5 Author Orcid Image
Article Authors Cited by (19) Tweetations (2) Metrics

Journals

  1. Zhang X, Xue Y, Su X, Chen S, Liu K, Chen W, Liu M, Hu Y. A Transfer Learning Approach to Correct the Temporal Performance Drift of Clinical Prediction Models: Retrospective Cohort Study. JMIR Medical Informatics 2022;10(11):e38053 View
  2. Gottlieb E, Samuel M, Bonventre J, Celi L, Mattie H. Machine Learning for Acute Kidney Injury Prediction in the Intensive Care Unit. Advances in Chronic Kidney Disease 2022;29(5):431 View
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  4. Ma L, Zhang C, Gao J, Jiao X, Yu Z, Zhu Y, Wang T, Ma X, Wang Y, Tang W, Zhao X, Ruan W, Wang T. Mortality prediction with adaptive feature importance recalibration for peritoneal dialysis patients. Patterns 2023;4(12):100892 View
  5. Do T, Yang H, Kim S, Kho B, Park J. Multi-horizon event detection for in-hospital clinical deterioration using dual-channel graph attention network. International Journal of Medical Informatics 2025;195:105745 View
  6. Szumilas M. Biosignal-Based Machine Learning Predictors of Sepsis: A Mini-Review. Acta Physica Polonica A 2024;146(4):388 View
  7. Ding S, Ye J, Hu X, Zou N. Distilling the knowledge from large-language model for health event prediction. Scientific Reports 2024;14(1) View
  8. Sim T, Hahn S, Kim K, Cho E, Jeong Y, Kim J, Ha E, Kim I, Park S, Cho C, Yu G, Cho H, Lee K. Preserving Informative Presence: How Missing Data and Imputation Strategies Affect the Performance of an AI-Based Early Warning Score. Journal of Clinical Medicine 2025;14(7):2213 View
  9. Christ M, Schmid N, Alscher M, Heidrich C, Rylski B, Latus J, Goebel N, Schanz M. Attention to early stages: predicting acute kidney injury in a post cardiosurgical ICU setting using an inclusive time-to-event model. Computers in Biology and Medicine 2025;192:110336 View
  10. Wu J, He K, Mao R, Shang X, Cambria E. Harnessing the potential of multimodal EHR data: A comprehensive survey of clinical predictive modeling for intelligent healthcare. Information Fusion 2025;123:103283 View
  11. Cujilan Guamán J, Chele Sudiaga N, Gavilanes Burnhan V, Tacle Flores J, Boza Ruiz R. Impacto de la inteligencia artificial en la predicción de eventos críticos en las unidades de cuidados intensivos: implicaciones para la práctica y la toma de decisiones en enfermería. Prohominum 2025;7(2):209 View
  12. Sim T, Cho E, Kim J, Lee K, Kim K, Hahn S, Ha E, Yun E, Kim I, Park S, Cho C, Yu G, Ahn B, Jeong Y, Won J, Cho H, Lee K. Prospective external validation of a deep-learning-based early-warning system for major adverse events in general wards in South Korea. Acute and Critical Care 2025;40(2):197 View
  13. Kim Y, Kim M, Kim Y, Choi M. Using nursing data for machine learning-based prediction modeling in intensive care units: A scoping review. International Journal of Nursing Studies 2025;169:105133 View
  14. Sim T, Cho E, Kim J, Kim H, Kim S. Clinical Context Is More Important than Data Quantity to the Performance of an Artificial Intelligence-Based Early Warning System. Journal of Clinical Medicine 2025;14(13):4444 View
  15. Kim J, Lee K, Kim K, Ha E, Kim I, Park S, Cho C, Yu G, Ahn B, Jeong Y, Won J, Sim T, Cho H, Lee K. Validation of an artificial intelligence-based algorithm for predictive performance and risk stratification of sepsis using real-world data from hospitalised patients: a prospective observational study. BMJ Health & Care Informatics 2025;32(1):e101353 View
  16. Sim T, Kim J, Cho E, Choi Y, Lee K, Kim K, Won J, Kim H, Cheon S, Kim Y. Deep Learning–Based Early Warning Systems in Hospitalized Patients at Risk of Code Blue Events and Length of Stay: Retrospective Real-world Implementation Study (Preprint). JMIR Medical Informatics 2025 View
  17. Cujilan Guamán J, Chele Sudiaga N, Gavilanes Burnhan V, Tacle Flores J, Boza Ruiz R. Impacto de la inteligencia artificial en la predicción de eventos críticos en las unidades de cuidados intensivos: Implicaciones para la práctica y la toma de decisiones en enfermería. Más Vita 2025;7(2):58 View

Books/Policy Documents

  1. Lee K, Won J, Hyun H, Hahn S, Choi E, Lee J. Trustworthy Machine Learning for Healthcare. View

Conference Proceedings

  1. Caron N, Guyeux C, Aynes B. Proceedings of the 2024 16th International Conference on Machine Learning and Computing. Predicting wildfire events with calibrated probabilities View