Towards Robust Time-to-Event Prediction: Integrating the Variational Information Bottleneck with Neural Survival Model
Bibliographic record
Abstract
Survival analysis aims to predict the time until a specific event of interest occurs. Although neural network-based survival models perform well in extracting rich feature embeddings and outperform traditional models, they are susceptible to the intricacies of noise present in real-world data. This noise can cause these models to miss crucial information for event-time prediction while introducing irrelevant information into the feature embeddings. Furthermore, models may struggle to distinguish between relevant and irrelevant information in data-limited regimes, such as healthcare. This can lead to overfitting, resulting from spurious correlations between irrelevant information and survival outcomes. To address these problems, we introduce the Variational Information Bottleneck (VIB) regularization approach. VIB is designed to meticulously filter out both irrelevant and redundant information, resulting in more robust feature embeddings for event-time prediction. We conducted detailed experiments on several real-world survival datasets. Our approach outperforms state-of-the-art methods in event-time prediction in various evaluation metrics. Furthermore, evaluations on semi-synthetic noisy dataset demonstrate the superior noise resistance of our approach, showcasing improved generalization and robustness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".