Réseaux bayésiens pour la classification Méthodologie et illustration dans le cadre du diagnostic médical
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
Les reseaux bayesiens sont des outils privilegies pour les problemes de diagnostic. Nous dressons dans cet article un panorama des algorithmes utilises classiquement pour la mise en oeuvre des reseaux bayesiens dans le cadre du diagnostic, et plus particulierement du diagnostic medical. Pour cela, nous passons en revue un certain nombre de questions methodologiques concernant le choix de la representation des densites de probabilite (faut-il discretiser les variables continues ? utiliser un modele gaussien ?) et surtout la determination de la structure du reseau bayesien (faut-il utiliser un reseau naif ou essayer d'apprendre une meilleure structure a l'aide d'un expert ou de donnees ?). Une etude de cas concernant le diagnostic de cancer de la thyroide nous permettra d'illustrer une partie de ces interrogations et des solutions proposees.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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