ArticleJMIR medical informatics2021
Event Prediction Model Considering Time and Input Error Using Electronic Medical Records in the Intensive Care Unit: Retrospective Study.
Article in JMIR medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Value of AI in Critical Care Using Real-World Evidence on Intensive Care Unit Mortality Prediction: Cost-Utility Analysis.Journal of medical Internet research · 2026Article
- Evaluating the Predictive Potential of an AI-Driven Deep Learning Model for Pneumonia-Associated Sepsis.Journal of clinical medicine · 2026Article
- Deep Learning-Based Early Warning Systems in Hospitalized Patients at Risk of Code Blue Events and Length of Stay: Retrospective Real-World Implementation Study.JMIR medical informatics · 2025Article
- Clinical Context Is More Important than Data Quantity to the Performance of an Artificial Intelligence-Based Early Warning System.Journal of clinical medicine · 2025Article
- 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 · 2025Observational
- 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 · 2025Article
- Preserving Informative Presence: How Missing Data and Imputation Strategies Affect the Performance of an AI-Based Early Warning Score.Journal of clinical medicine · 2025Article
- Distilling the knowledge from large-language model for health event prediction.Scientific reports · 2024Article
- Mortality prediction with adaptive feature importance recalibration for peritoneal dialysis patients.Patterns (New York, N.Y.) · 2023Article
- A Transfer Learning Approach to Correct the Temporal Performance Drift of Clinical Prediction Models: Retrospective Cohort Study.JMIR medical informatics · 2022Article
- Machine Learning for Acute Kidney Injury Prediction in the Intensive Care Unit.Advances in chronic kidney disease · 2022Review
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9 authors.
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Abstract
backgroundIn the era of artificial intelligence, event prediction models are abundant. However, considering the limitation of the electronic medical record-based model, including the temporally skewed prediction and the record itself, these models could be delayed or could yield errors.
objectiveIn this study, we aim to develop multiple event prediction models in intensive care units to overcome their temporal skewness and evaluate their robustness against delayed and erroneous input.
methodsA total of 21,738 patients were included in the development cohort. Three events-death, sepsis, and acute kidney injury-were predicted. To overcome the temporal skewness, we developed three models for each event, which predicted the events in advance of three prespecified timepoints. Additionally, to evaluate the robustness against input error and delays, we added simulated errors and delayed input and calculated changes in the area under the receiver operating characteristic curve (AUROC) values.
resultsMost of the AUROC and area under the precision-recall curve values of each model were higher than those of the conventional scores, as well as other machine learning models previously used. In the error input experiment, except for our proposed model, an increase in the noise added to the model lowered the resulting AUROC value. However, the delayed input did not show the performance decreased in this experiment.
conclusionsFor a prediction model that was applicable in the real world, we considered not only performance but also temporal skewness, delayed input, and input error.
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