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
.

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
- 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
- Szumilas M. Biosignal-Based Machine Learning Predictors of Sepsis: A Mini-Review. Acta Physica Polonica A 2024;146(4):388 View
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Lee K, Won J, Hyun H, Hahn S, Choi E, Lee J. Trustworthy Machine Learning for Healthcare. View
Conference Proceedings
- 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