{"slug": "deep-label-wise-attentive-temporal-convolutional-networks-improve-medical-coding", "title": "Deep Label-Wise Attentive Temporal Convolutional Networks Improve Medical Coding", "summary": "A deep neural model combining multi-layer temporal convolution networks with label-wise attention improves medical coding accuracy, achieving a 9% increase in F-1 scores and a 28% increase in recall over the previous state-of-the-art, according to a new arXiv preprint (2607.25129v1). The model addresses the challenge of aggregating information from different parts of clinical notes for each diagnosis and procedure code.", "body_md": "arXiv:2607.25129v1 Announce Type: new\nAbstract: Medical coding is the task of assigning a set of diagnosis and procedure codes for a hospitalization using recorded notes. It requires aggregating information from different parts of the text and focus to different sections for each individual code, making it a very difficult problem even for professional human coders. We model the task as a multi-label text classification problem. To overcome the mentioned difficulties, we propose a deep neural model consisting of a multi-layer temporal convolution network (TCN) followed by label-wise attention. While multi-layer TCN helps extract a global document representation with the ability to learn relations over very long sequences, label-specific attention mechanism allows the model to focus on different aspects of the same document for each individual label. Our method achieves significantly better F-1 scores (9% increase) compared to the previous state-of-the-art model, with a remarkable increase in recall score (28% increase), which we believe is the more important metric for a clinical decision support setting.", "url": "https://wpnews.pro/news/deep-label-wise-attentive-temporal-convolutional-networks-improve-medical-coding", "canonical_source": "https://arxiv.org/abs/2607.25129", "published_at": "2026-07-29 04:00:00+00:00", "updated_at": "2026-07-29 04:26:39.532113+00:00", "lang": "en", "topics": ["machine-learning", "natural-language-processing", "ai-research"], "entities": ["arXiv"], "alternates": {"html": "https://wpnews.pro/news/deep-label-wise-attentive-temporal-convolutional-networks-improve-medical-coding", "markdown": "https://wpnews.pro/news/deep-label-wise-attentive-temporal-convolutional-networks-improve-medical-coding.md", "text": "https://wpnews.pro/news/deep-label-wise-attentive-temporal-convolutional-networks-improve-medical-coding.txt", "jsonld": "https://wpnews.pro/news/deep-label-wise-attentive-temporal-convolutional-networks-improve-medical-coding.jsonld"}}