Multilabel associative classification categorization of MEDLINE aticles into MeSH keywords
Bibliographic record
Abstract
The specific characteristic of classification of medical documents from the MEDLINE database is that each document is assigned to more than one category, which requires a system for multilabel classification. Another major challenge was to develop a scalable method capable of dealing with hundreds of thousand of documents. We proposed a novel system for automated classification of MEDLINE documents to MeSH keywords based on the recently developed data mining algorithm called ACRI, which was modified to accommodate multilabel classification. Five different classification configurations in conjunction with different methods of measuring classification quality were proposed and tested. The extensive experimental comparison showed superiority of methods based on reoccurrence of words in an article over nonrecurrent-based associative classification. The achieved relatively high value of macro F1 (46%) demonstrates the high quality of the proposed system for this challenging dataset. Accuracy of the proposed classifier, defined as the ratio of the sum of TP and TN examples to the total number of examples, reached 90%. Three scenarios were proposed based on the performed tests and different possible objectives. If a goal is to classify the largest number of documents, a configuration that maximizes micro F1 should be chosen. On the other hand, if a system is to work well for categories with a small number of documents, a configuration that maximizes macro F1 is more suitable. A tradeoff can be obtained by using a configuration that optimizes the average between macro and micro F1.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".