2026-07-29 04:00 UTCOriginal source2 min readUpdated: 2026-07-29 08:34 UTC

Deep Label-Wise Attentive Temporal Convolutional Networks Improve Medical Coding

A new deep learning model combining temporal convolutional networks with label-wise attention significantly improves medical coding accuracy, achieving a 9% increase in F1 score and a 28% increase in recall over previous methods.

SourcearXiv Computational LinguisticsAuthor: Muhammed Yavuz Nuzumlal{\i}, Alexander Fabbri, Irene Li, Dragomir Radev

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[Submitted on 27 Jul 2026]

Title:Deep Label-Wise Attentive Temporal Convolutional Networks Improve Medical Coding

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Abstract: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.

Comments: Work carried out in 2019; posted as a record of the work. Baselines and state of the art reflect the 2019 literature

Subjects:

Computation and Language (cs.CL); Machine Learning (cs.LG)

Cite as: arXiv:2607.25129 [cs.CL]

(or arXiv:2607.25129v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.25129

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Muhammed Yavuz Nuzumlalı [view email] [v1] Mon, 27 Jul 2026 22:48:05 UTC (64 KB)

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