Development of International Classification of Diseases crosswalks using text analysis methods.
Bibliographic record
Abstract
ObjectiveTo evaluate the performance of a natural language processing (NLP) method to develop an automated crosswalk between the 9th and 10th revisions of the International Classification of Diseases (ICD) for diagnosis codes in the Charlson comorbidity index (CCI). ApproachSBERT, an advanced NLP transformer-based model, was used to produce sentence embeddings, numeric vectors that represent the semantic meaning of text, for the labels (i.e., descriptors) of 932 ICD-10-CA (Canadian Adaptation) codes in the CCI (up to six digits). Sentence embeddings were also produced for all ICD-9-CM (Clinical Modification) code labels (15,145). Cosine similarity scores (CSS) were calculated for all possible pairs of ICD-10-CA and ICD-9-CM code labels. CSSs were classified as equivalent (CSS = 1), high (0.8 ≤ CSS < 1), and low (CSS < 0.8). CSS categories for CCI diagnosis codes were compared to an ICD-9-CM to ICD-10-CA crosswalk file manually created by the Canadian Institute of Health Information. ResultsOf the 932 CSSs for ICD-10-CA codes in CCI, 84 (9%) were classified as equivalent, 284 (30.5%) were high, and 564 (60.5%) were low. For ICD-10-CA codes with low CSSs, the median was 0.67 (interquartile range 0.14). Conclusions and ImplicationsAn ICD-10-CA to ICD-9-CM crosswalk based on NLP had low accuracy for identifying semantically similar diagnosis code labels. The accuracy of this method might be improved by fine-tuning and training on task-specific data. Evaluation of different text analysis-based models would provide guidance for research involving ICD code labels.
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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 | 0.020 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.017 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".