TACL: Threshold-Adaptive Curriculum Learning Strategy for Enhancing Medical Text Understanding
Bibliographic record
Abstract
Electronic medical records (EMRs) are crucial for modern healthcare, containing rich information about patient care, diagnoses, and treatments. However, their unstructured nature, domain-specific language, and complexity pose significant challenges for automated understanding. Existing methods often treat all data equally, limiting their ability to handle rare or complex cases effectively. We present TACL (Threshold-Adaptive Curriculum Learning), a novel framework that dynamically adjusts the training process based on sample complexity. Inspired by progressive learning, TACL categorizes data into difficulty levels, focusing on simpler cases early in training and gradually addressing more complex ones. A domain-specific pre-trained language model is used for difficulty assessment, considering semantic, syntactic, and contextual features. Additionally, TACL employs an adaptive training strategy to enhance task-specific performance and ensure generalization across diverse datasets. Experimental results on multilingual datasets, including MIMICIII, MIMIC-IV, and Chinese clinical records, demonstrate TACL's effectiveness in tasks such as ICD coding, readmission prediction, and TCM syndrome differentiation. TACL improves performance on rare and complex cases, providing a scalable and robust solution for medical text understanding.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".