Effective Convolutional Attention Network for Multi-label Clinical Document Classification

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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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Medical Code Prediction MIMIC-III EffectiveCAN Macro-AUC 91.5 # 6
Micro-AUC 98.8 # 5
Macro-F1 10.6 # 5
Micro-F1 58.9 # 2
Precision@8 75.8 # 3
Precision@15 60.6 # 3


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