HitBoost: Survival Analysis via a Multi-Output Gradient Boosting Decision Tree Method
Bibliographic record
Abstract
Survival analysis, in many areas such as healthcare and finance, mainly studies the probability of time to the event of interest. Among various methods that build survival predictive models, a class of methods combining with machine learning techniques make assumptions about hazard functions, while another class of methods directly exploit complex neural networks to learn the latent representation of hazard functions. For the traditional survival predictive models, the assumption about hazard functions restricts their performance to some extends. Similarly, the advanced survival predictive models built by complex neural networks also suffer from fairly poor interpretation in real applications. To solve these problems, in this paper, a novel survival analysis method named HitBoost is proposed to predict the probability distribution of the first hitting time (FHT). Instead of making any assumptions about the underlying stochastic process, the proposed HitBoost adopts the multi-output gradient boosting decision tree to implicitly capture the connections between the static covariate and the underlying stochastic process. Furthermore, in the process of tree boosting, the relevant statistics can be utilized to effectively measure the feature importance. The results of evaluations and case studies on benchmarks show that, in comparison to the classical methods, the proposed HitBoost is superior in prediction performance and risk discrimination. Therefore, the HitBoost can be utilized as an effective method to build survival predictive models or to find the important factors for cause-specific failure.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".