Automated ICD-9 Coding via A Deep Learning Approach
Bibliographic record
Abstract
ICD-9 (the Ninth Revision of International Classification of Diseases) is widely used to describe a patient's diagnosis. Accurate automated ICD-9 coding is important because manual coding is expensive, time-consuming, and inefficient. Inspired by the recent successes of deep learning, in this study, we present a deep learning framework called DeepLabeler to automatically assign ICD-9 codes. DeepLabeler combines the convolutional neural network with the 'Document to Vector' technique to extract and encode local and global features. Our proposed DeepLabeler demonstrates its effectiveness by achieving state-of-the-art performance, i.e., 0.335 micro F-measure on MIMIC-II dataset and 0.408 micro F-measure on MIMIC-III dataset. It outperforms classical hierarchy-based SVM and flat-SVM both on these two datasets by at least 14 percent. Furthermore, we analyze the deep neural network structure to discover the vital elements in the success of DeepLabeler. We find that the convolutional neural network is the most effective component in our network and the 'Document to Vector' technique is also necessary for enhancing classification performance since it extracts well-recognized global features. Extensive experimental results demonstrate that the great promise of deep learning techniques in the field of text multi-label classification and automated medical coding.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| 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) | 0.004 | 0.002 |
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".