SynthesisApplied clinical informatics2021
Systematic Review of Approaches to Preserve Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine.
Synthesis in Applied clinical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Digital management of diabetes global research trends: a bibliometric study.Frontiers in medicine · 2025Pooled it
- Evaluation of domain generalization and adaptation on improving model robustness to temporal dataset shift in clinical medicine.Scientific reports · 2022Trial
- Comparison of Methods for Incorporating Related Data When Developing Clinical Prediction Models: A Simulation Study.Biometrical journal. Biometrische Zeitschrift · 2026Article
- A machine learning model for in-hospital mortality prediction in patients with sepsis-related myocardial injury.PLOS digital health · 2026Article
- Beyond Regulatory Approval: Lifecycle Assurance for Clinical Artificial Intelligence.Journal of medical systems · 2026Article
- Artificial Intelligence and Bioengineering Approaches for Antimicrobial Resistance Prediction.Medicina (Kaunas, Lithuania) · 2026Review
- Autonomous Agents for Auditable Cardiovascular Artificial Intelligence Development.medRxiv : the preprint server for health sciences · 2026Article
- Beyond Model Development in Healthcare AI: Post-Development Robustness, Post-Deployment Monitoring, and Lifecycle Governance-A Scoping Review of Reviews.Healthcare (Basel, Switzerland) · 2026Review
- Clinical-ShiftEval: a framework for simulating and evaluating model adaptation in dynamic clinical NLP tasks.BMC medical informatics and decision making · 2026Article
- Enhancing Model Generalizability in Medical Artificial Intelligence: Systematic Comparison of Categorical Encoding and Sampling Techniques for Imbalanced Data.JMIR medical informatics · 2026Article
- Unsupervised Characterization of Temporal Dataset Shifts as an Early Indicator of AI Performance Variations: Evaluation Study Using the Medical Information Mart for Intensive Care-IV Dataset.JMIR medical informatics · 2025Article
- Development and prospective evaluation of a machine learning model to predict vomiting among pediatric cancer and hematopoietic cell transplant patients.BMC cancer · 2025Article
- Clinician-in-the-loop screening saturation: predicting annotation yield for efficient EHR review.BMC medical informatics and decision making · 2025Article
- Artificial intelligence in cancer: applications, challenges, and future perspectives.Molecular cancer · 2025Review
- Monitoring strategies for continuous evaluation of deployed clinical prediction models.Journal of biomedical informatics · 2025Article
- Correcting for case-mix shift when developing clinical prediction models.BMC medical research methodology · 2025Article
- Diagnostic framework to validate clinical machine learning models locally on temporally stamped data.Communications medicine · 2025Article
- Harnessing genotype and phenotype data for population-scale variant classification using large language models and bayesian inference.Human genetics · 2025Article
- Hospital-wide, dynamic, individualized prediction of central line-associated bloodstream infections-development and temporal evaluation of six prediction models.BMC infectious diseases · 2025Article
- One-class support vector machines for detecting population drift in deployed machine learning medical diagnostics.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe change in performance of machine learning models over time as a result of temporal dataset shift is a barrier to machine learning-derived models facilitating decision-making in clinical practice. Our aim was to describe technical procedures used to preserve the performance of machine learning models in the presence of temporal dataset shifts.
methodsStudies were included if they were fully published articles that used machine learning and implemented a procedure to mitigate the effects of temporal dataset shift in a clinical setting. We described how dataset shift was measured, the procedures used to preserve model performance, and their effects.
resultsOf 4,457 potentially relevant publications identified, 15 were included. The impact of temporal dataset shift was primarily quantified using changes, usually deterioration, in calibration or discrimination. Calibration deterioration was more common (
conclusionThere was limited research in preserving the performance of machine learning models in the presence of temporal dataset shift in clinical medicine. Future research could focus on the impact of dataset shift on clinical decision making, benchmark the mitigation strategies on a wider range of datasets and tasks, and identify optimal strategies for specific settings.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.