Analyzing systemic lupus erythematosus publications using neural network–based multi-label classification algorithms
Bibliographic record
Abstract
The heterogeneity in systemic lupus erythematosus research topics poses a challenge for the entire lupus community, from basic geneticists to clinical investigators. As such, it is critical for medical professionals to remain up to date on directions in lupus research and the main fields in which this research is being conducted (e.g., etiology, diagnosis, treatment, and outcomes). This article develops two multi-label text-classification models using Deep Neural Networks and Convolutional Neural Networks to classify the human-based adult-onset lupus-related articles in the PubMed database based on their abstract, keywords, and MeSH terms. During training evaluation, our models correctly indicated all relevant labels for 70% of the articles. The applied machine learning models (Deep Neural Network and Convolutional Neural Network) yielded a Micro-F1 score of 0.89, meaning that it successfully labeled the most relevant medical domains and types. In addition, these types of studies help the researchers be aware of the essential topics in this field, but due to difficulties in designing, the related studies are ignored or fade.
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.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".