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](https://asset.jmir.pub/assets/0a8f1353df922574ea84339c468caae9.png 480w,https://asset.jmir.pub/assets/0a8f1353df922574ea84339c468caae9.png 960w,https://asset.jmir.pub/assets/0a8f1353df922574ea84339c468caae9.png 1920w,https://asset.jmir.pub/assets/0a8f1353df922574ea84339c468caae9.png 2500w)
Journals
- 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
- 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
- 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
- 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
- Lee K, Won J, Hyun H, Hahn S, Choi E, Lee J. Trustworthy Machine Learning for Healthcare. View