Combining Ranking and Point-wise Losses for Training Deep Survival Analysis Models
Bibliographic record
Abstract
Being able to accurately predict the time to event of interest, commonly known as survival analysis, is extremely beneficial in many real-world applications. Traditional commonly used statistical survival analysis methods, e.g., Cox proportional hazards model and parametric censored regressions, are based on strong and sometimes impractical assumptions and can only handle linearity relationship between features and target. Recently, deep learning based formulations have been proposed for survival analysis to handle non-linearity. However, these existing deep learning methods either inherit strong assumptions from their corresponding base models or tailor discrete-time survival analysis. To overcome the limitations within these existing models in the literature, we propose an objective function to guide the training of a deep learning model for continuous-time survival analysis. The objective function combines both ranking based and point-wise regression based losses. The ranking based loss measures the goodness of the orders of the predicted survival time for all instances. The point-wise based loss measures the difference between the predicted survival time and the true survival time for the right censored time-to-event data. More specifically, we derive two versions of the ranking based loss from the smoothed concordance index, and two versions of point-wise based loss based on the normalized mean squared error (MSE) and mean absolute error (MAE). Thus, the proposed formulation is capable of dealing with the continuous-time survival analysis from both global and local perspectives. We conduct experimental analysis over several large-scale real-world time-to-event datasets, and the results demonstrate that our model outperforms the state-of-the-art survival analysis methods. The codes and data used in the experiments are available in the link1.1https://github.com/yanlirock/local_global_survival
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| 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 | 0.002 | 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".