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

Machine Learning (1465) Big Data (240) Electronic Health Records (1053) Intensive Care Unit (ICU) (112) Clinical Information and Decision Making (1407) Clinical Informatics (1022)

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 (6) 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
  3. Do T, Yang H, Lee G, Kim S, Kho B. Rapid Response System Based on Graph Attention Network for Predicting In-Hospital Clinical Deterioration. IEEE Access 2023;11:29091 View
  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

Books/Policy Documents

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