A Bayesian network model for analysis of detection performance in surveillance systems.
Bibliographic record
Abstract
Worldwide developments concerning infectious diseases and bioterrorism are driving forces for improving aberrancy detection in public health surveillance. The performance of an aberrancy detection algorithm can be measured in terms of sensitivity, specificity and timeliness. However, these metrics are probabilistically dependent variables and there is always a trade-off between them. This situation raises the question of how to quantify this tradeoff. The answer to this question depends on the characteristics of the specific disease under surveillance, the characteristics of data used for surveillance, and the algorithmic properties of detection methods. In practice, the evidence describing the relative performance of different algorithms remains fragmented and mainly qualitative. In this paper, we consider the development and evaluation of a Bayesian network framework for analysis of performance measures of aberrancy detection algorithms. This framework enables principled comparison of algorithms and identification of suitable algorithms for use in specific public health surveillance settings.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".