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
Bibliographic record
Abstract
BACKGROUND: Reusing long-term data from electronic health records is essential for training reliable and effective health artificial intelligence (AI). However, intrinsic changes in health data distributions over time-known as dataset shifts, which include concept, covariate, and prior shifts-can compromise model performance, leading to model obsolescence and inaccurate decisions. OBJECTIVE: In this study, we investigate whether unsupervised, model-agnostic characterization of temporal dataset shifts using data distribution analyses through Information Geometric Temporal (IGT) projections is an early indicator of potential AI performance variations before model development. METHODS: Using the real-world Medical Information Mart for Intensive Care-IV (MIMIC-IV) electronic health record database, encompassing data from over 40,000 patients from 2008 to 2019, we characterized its inherent dataset shift patterns through an unsupervised approach using IGT projections and data temporal heatmaps. We trained and evaluated annually a set of random forests and gradient boosting models to predict in-hospital mortality. To assess the impact of shifts on model performance, we checked the association between the temporal clusters found in both IGT projections and the intertime embedding of model performances using the Fisher exact test. RESULTS: Our results demonstrate a significant relationship between the unsupervised temporal shift patterns, specifically covariate and concept shifts, identified using the IGT projection method and the performance of the random forest and gradient boosting models (P<.05). We identified 2 primary temporal clusters that correspond to the periods before and after ICD-10 (International Statistical Classification of Diseases, Tenth Revision) implementation. The transition from ICD-9 (International Classification of Diseases, Ninth Revision) to ICD-10 was a major source of dataset shift, associated with a performance degradation. CONCLUSIONS: Unsupervised, model-agnostic characterization of temporal shifts via IGT projections can serve as a proactive monitoring tool to anticipate performance shifts in clinical AI models. By incorporating early shift detection into the development pipeline, we can enhance decision-making during the training and maintenance of these models. This approach paves the way for more robust, trustworthy, and self-adapting AI systems in health care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".