Multilabel associative classification categorization of MEDLINE aticles into MeSH keywords
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
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.
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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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