ICDBigBird: A Contextual Embedding Model for ICD Code Classification
Bibliographic record
Abstract
The International Classification of Diseases (ICD) system is the international standard for classifying diseases and procedures during a healthcare encounter and is widely used for healthcare reporting and management purposes.Assigning correct codes for clinical procedures is important for clinical, operational and financial decision-making in healthcare.Contextual word embedding models have achieved state-of-the-art results in multiple NLP tasks.However, these models have yet to achieve state-of-the-art results in the ICD classification task since one of their main disadvantages is that they can only process documents that contain a small number of tokens which is rarely the case with real patient notes.In this paper, we introduce ICDBigBird a BigBird-based model which can integrate a Graph Convolutional Network (GCN), that takes advantage of the relations between ICD codes in order to create 'enriched' representations of their embeddings, with a BigBird contextual model that can process larger documents.Our experiments on a real-world clinical dataset demonstrate the effectiveness of our BigBird-based model on the ICD classification task as it outperforms the previous state-of-the-art models.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".