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A Transfer Learning Approach to Correct the Temporal Performance Drift of Clinical Prediction Models: Retrospective Cohort Study
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1001/jama.2020.1230openalex-snapshot
Validation and Utility Testing of Clinical Prediction Models
inferredcites →https://doi.org/10.1007/s00134-015-3934-7openalex-snapshot
Epidemiology of acute kidney injury in critically ill patients: the multinational AKI-EPI study
inferredcites →https://doi.org/10.1007/s10916-018-1109-0openalex-snapshot
Prediction of Incident Delirium Using a Random Forest classifier
inferredcites →https://doi.org/10.1007/s40031-014-0176-yopenalex-snapshot
Weighted Hybrid Decision Tree Model for Random Forest Classifier
inferredcites →https://doi.org/10.1016/j.jbi.2020.103611openalex-snapshot
Detection of calibration drift in clinical prediction models to inform model updating
inferredcites →https://doi.org/10.1016/j.jclinepi.2014.06.018openalex-snapshot
A new framework to enhance the interpretation of external validation studies of clinical prediction models
inferredcites →https://doi.org/10.1038/s41467-020-19551-wopenalex-snapshot
Cross-site transportability of an explainable artificial intelligence model for acute kidney injury prediction
inferredcites →https://doi.org/10.1038/s41586-019-1390-1openalex-snapshot
A clinically applicable approach to continuous prediction of future acute kidney injury
inferredcites →https://doi.org/10.1038/s41598-018-21929-2openalex-snapshot
Acute Kidney Injury in Trauma Patients Admitted to Critical Care: Development and Validation of a Diagnostic Prediction Model
inferredcites →https://doi.org/10.1055/s-0037-1606495openalex-snapshot
Representing Knowledge Consistently Across Health Systems
inferredcites →https://doi.org/10.1093/jamia/ocx030openalex-snapshot
Calibration drift in regression and machine learning models for acute kidney injury
inferredcites →https://doi.org/10.1093/jamia/ocz127openalex-snapshot
A nonparametric updating method to correct clinical prediction model drift
inferredcites →https://doi.org/10.1093/jamiaopen/ooy043openalex-snapshot
Multi-perspective predictive modeling for acute kidney injury in general hospital populations using electronic medical records
inferredcites →https://doi.org/10.1097/ccm.0000000000003123openalex-snapshot
The Development of a Machine Learning Inpatient Acute Kidney Injury Prediction Model*
inferredcites →https://doi.org/10.1109/iccv.2013.274openalex-snapshot
Transfer Feature Learning with Joint Distribution Adaptation
inferredcites →https://doi.org/10.1109/jbhi.2017.2767063openalex-snapshot
Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis
inferredcites →https://doi.org/10.1109/jproc.2020.3004555openalex-snapshot
A Comprehensive Survey on Transfer Learning
inferredcites →https://doi.org/10.1109/tai.2021.3054609openalex-snapshot
A Decade Survey of Transfer Learning (2010–2020)
inferredcites →https://doi.org/10.1109/tcbb.2019.2911071openalex-snapshot
XGBoost Model for Chronic Kidney Disease Diagnosis
inferredcites →https://doi.org/10.1109/tpami.2016.2618118openalex-snapshot
Learn on Source, Refine on Target: A Model Transfer Learning Framework with Random Forests
inferredcites →https://doi.org/10.1111/j.1466-8238.2011.00683.xopenalex-snapshot
Insights into the area under the receiver operating characteristic curve (AUC) as a discrimination measure in species distribution modelling
inferredcites →https://doi.org/10.1136/amiajnl-2013-002162openalex-snapshot
A study in transfer learning: leveraging data from multiple hospitals to enhance hospital-specific predictions
inferredcites →https://doi.org/10.1136/bmj.b606openalex-snapshot
Prognosis and prognostic research: application and impact of prognostic models in clinical practice
inferredcites →https://doi.org/10.1136/heartjnl-2011-301247openalex-snapshot
Risk prediction models: II. External validation, model updating, and impact assessment
inferredcites →https://doi.org/10.1145/1273496.1273521openalex-snapshot
Boosting for transfer learning
inferredcites →https://doi.org/10.1145/3127881openalex-snapshot
Mining Electronic Health Records (EHRs)
inferredcites →https://doi.org/10.1159/000339789openalex-snapshot
KDIGO Clinical Practice Guidelines for Acute Kidney Injury
inferredcites →https://doi.org/10.1161/circoutcomes.114.001645openalex-snapshot
Improved Prediction by Dynamic Modeling
inferredcites →https://doi.org/10.1186/s12911-021-01740-2openalex-snapshot
Machine learning model for predicting acute kidney injury progression in critically ill patients
inferredcites →https://doi.org/10.1186/s40537-016-0043-6openalex-snapshot
A survey of transfer learning
inferredcites →https://doi.org/10.2196/16903openalex-snapshot
Machine Learning Model for Risk Prediction of Community-Acquired Acute Kidney Injury Hospitalization From Electronic Health Records: Development and Validation Study
inferredcites →https://doi.org/10.2196/24120openalex-snapshot
Real-Time Clinical Decision Support Based on Recurrent Neural Networks for In-Hospital Acute Kidney Injury: External Validation and Model Interpretation
inferredcites →https://doi.org/10.2196/26426openalex-snapshot
Event Prediction Model Considering Time and Input Error Using Electronic Medical Records in the Intensive Care Unit: Retrospective Study
inferredcites →https://doi.org/10.2215/cjn.02730313openalex-snapshot
Incidence, Outcomes, and Comparisons across Definitions of AKI in Hospitalized Individuals
inferredcites →https://doi.org/10.2307/1165085openalex-snapshot
It's about Time: Using Discrete-Time Survival Analysis to Study Duration and the Timing of Events
inferredcites →https://doi.org/10.5555/1953048.2078195openalex-snapshot
Scikit-learn: Machine Learning in Python
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Comprehensive Overview of Computational Modeling and Artificial Intelligence in Pediatric Neurosurgery
inferred← citeshttps://doi.org/10.1007/s10916-026-02466-2openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s11255-025-04878-4openalex-snapshot
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Temporal adaptive transfer learning for clinical language models
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Personalized early prediction of weight loss success using transfer learning and explainable GRU-attention networks
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Machine Learning Operations in Health Care: A Scoping Review
inferred← citeshttps://doi.org/10.1093/jamia/ocag025openalex-snapshot
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inferred← citeshttps://doi.org/10.21203/rs.3.rs-7151065/v1openalex-snapshot
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Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Models (CREMLS)
inferred← citeshttps://doi.org/10.2196/55734openalex-snapshot
Development and Validation of a Machine Learning–Based Early Warning Model for Lichenoid Vulvar Disease: Prediction Model Development Study
inferred← citeshttps://doi.org/10.2196/55744openalex-snapshot
Retracted: Comparative Effectiveness of Health Communication Strategies in Nursing: A Mixed Methods Study of Internet, mHealth, and Social Media Versus Traditional Methods
inferred← citeshttps://doi.org/10.2196/preprints.52508openalex-snapshot
Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Models (CREMLS) (Preprint)
inferred← citeshttps://doi.org/10.2196/preprints.55744openalex-snapshot
Comparative Effectiveness: Health Communication on the Internet, mHealth, and Social Media vs. Traditional Methods - A Nursing Student's Perspective (Preprint)
inferred← citeshttps://doi.org/10.3389/frai.2026.1870819openalex-snapshot
Continuous assurance for AI-driven clinical decision support systems
inferred← citeshttps://doi.org/10.3390/nu18010023openalex-snapshot
Large Language Models for Real-World Nutrition Assessment: Structured Prompts, Multi-Model Validation and Expert Oversight
inferred← citeshttps://doi.org/10.34133/hds.0321openalex-snapshot
Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Structured Data Analysis
inferred← citeshttps://doi.org/10.37256/rrcs.2120232742openalex-snapshot
Chest Disease Image Classification Based on Spectral Clustering Algorithm
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