Optimized-Constrained Transfer Learning: Application for Stroke Prediction
Bibliographic record
Abstract
Today’s machine learning applications in the healthcare sector often require a vast amount of training data and lengthy training, which are not always available. Transfer learning (TL) offers a promising solution by leveraging knowledge across different domains/tasks, effectively addressing data scarcity. However, TL encounters computational and communication challenges in resource-constrained scenarios, and negative transfer (NT) can arise, posing significant challenges for sensitive tasks such as stroke prediction. This paper presents a novel method to maximize the accuracy of TL in resource-constrained environments while mitigating NT, a key concern in TL. Previous studies have overlooked simultaneously the resource consumption and the NT problem in the network-based TL era. To address this shortcoming, we formulate an optimization model named RCTL, which employs the empirical fine-tuning error of the target task as the objective while considering NT overcoming condition and resource limitations as the feasibility constraints. Our approach is extensively validated in a stroke prediction scenario, demonstrating the efficiency of RCTL in achieving the desired trade-offs between TL’s benefits and associated costs. The obtained results show the potential of enhancing edge AI applications in resource-constrained healthcare infrastructure.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".