Papers › Effective Convolutional Attention Network for Multi-label Clinical Document Classification
Effective Convolutional Attention Network for Multi-label Clinical Document Classification
Yang Liu, Hua Cheng, Russell Klopfer, Matthew R. Gormley, Thomas Schaaf
Multi-label document classification (MLDC) problems can be challenging, especially for long documents with a large label set and a long-tail distribution over labels. In this paper, we present an effective convolutional attention network for the MLDC problem with a focus on medical code prediction from clinical documents. Our innovations are three-fold: (1) we utilize a deep convolution-based encoder with the squeeze-and-excitation networks and residual networks to aggregate the information across the document and learn meaningful document representations that cover different ranges of texts; (2) we explore multi-layer and sum-pooling attention to extract the most informative features from these multi-scale representations; (3) we combine binary cross entropy loss and focal loss to improve performance for rare labels. We focus our evaluation study on MIMIC-III, a widely used dataset in the medical domain. Our models outperform prior work on medical coding and achieve new state-of-the-art results on multiple metrics. We also demonstrate the language independent nature of our approach by applying it to two non-English datasets. Our model outperforms prior best model and a multilingual Transformer model by a substantial margin.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Medical Code Prediction | MIMIC-III | EffectiveCAN | Macro-AUC | 91.5 | #4 of 18 | Archive leaderboard | report |
| Medical Code Prediction | MIMIC-III | EffectiveCAN | Macro-F1 | 10.6 | #4 of 18 | Archive leaderboard | report |
| Medical Code Prediction | MIMIC-III | EffectiveCAN | Micro-AUC | 98.8 | #4 of 18 | Archive leaderboard | report |
| Medical Code Prediction | MIMIC-III | EffectiveCAN | Micro-F1 | 58.9 | #4 of 18 | Archive leaderboard | report |
| Medical Code Prediction | MIMIC-III | EffectiveCAN | Precision@15 | 60.6 | #4 of 18 | Archive leaderboard | report |
| Medical Code Prediction | MIMIC-III | EffectiveCAN | Precision@8 | 75.8 | #4 of 18 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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