ALBERT-Based Self-Ensemble Model With Semisupervised Learning and Data Augmentation for Clinical Semantic Textual Similarity Calculation: Algorithm Validation Study
Bibliographic record
Abstract
BACKGROUND: In recent years, with increases in the amount of information available and the importance of information screening, increased attention has been paid to the calculation of textual semantic similarity. In the field of medicine, electronic medical records and medical research documents have become important data resources for clinical research. Medical textual semantic similarity calculation has become an urgent problem to be solved. OBJECTIVE: This research aims to solve 2 problems-(1) when the size of medical data sets is small, leading to insufficient learning with understanding of the models and (2) when information is lost in the process of long-distance propagation, causing the models to be unable to grasp key information. METHODS: This paper combines a text data augmentation method and a self-ensemble ALBERT model under semisupervised learning to perform clinical textual semantic similarity calculations. RESULTS: Compared with the methods in the 2019 National Natural Language Processing Clinical Challenges Open Health Natural Language Processing shared task Track on Clinical Semantic Textual Similarity, our method surpasses the best result by 2 percentage points and achieves a Pearson correlation coefficient of 0.92. CONCLUSIONS: When the size of medical data set is small, data augmentation can increase the size of the data set and improved semisupervised learning can boost the learning efficiency of the model. Additionally, self-ensemble methods improve the model performance. Our method had excellent performance and has great potential to improve related medical problems.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".