Interpretable incubation period prediction with gradient boosting acceleration and disjoint region optimization based on generalized additive model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 it