Interpretable incubation period prediction with gradient boosting acceleration and disjoint region optimization based on generalized additive model
Bibliographic record
Abstract
Predicting the incubation period of infectious diseases is critical for detecting latent infections. To address this problem, this paper proposes an interpretable machine learning approach called GB-GAMO with gradient boosting acceleration and disjoint region optimization. Gradient boosting acceleration by covariate selection is proposed to speed up the growth of shallow regression trees in training the generalized additive model of individual covariates. Those individual covariates with the largest contribution to loss minimization of greedy function approximation of negative gradients are assigned as the optimal split covariate. Disjoint region optimization is proposed to minimize the loss of residuals in training the generalized additive model on interaction terms. Those interaction terms whose shape functions can minimize the loss of time residuals are used to construct the generalized additive model with optimized weight settings. Experiments on the collected 519 confirmed COVID-19 cases demonstrate that GB-GAMO outperforms state-of-the-art methods in prediction accuracy and interpretability.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".