Wang, Zeqiang;
Wang, Yuqi;
Zhang, Haiyang;
Wang, Wei;
Qi, Jun;
Chen, Jianjun;
Sastry, Nishanth;
... De, Suparna; + view all
(2024)
ICDXML: enhancing ICD coding with probabilistic label trees and dynamic semantic representations.
Scientific Reports
, 14
(1)
, Article 18319. 10.1038/s41598-024-69214-9.
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Abstract
Accurately assigning standardized diagnosis and procedure codes from clinical text is crucial for healthcare applications. However, this remains challenging due to the complexity of medical language. This paper proposes a novel model that incorporates extreme multi-label classification tasks to enhance International Classification of Diseases (ICD) coding. The model utilizes deformable convolutional neural networks to fuse representations from hidden layer outputs of pre-trained language models and external medical knowledge embeddings fused using a multimodal approach to provide rich semantic encodings for each code. A probabilistic label tree is constructed based on the hierarchical structure existing in ICD labels to incorporate ontological relationships between ICD codes and enable structured output prediction. Experiments on medical code prediction on the MIMIC-III database demonstrate competitive performance, highlighting the benefits of this technique for robust clinical code assignment.
| Type: | Article |
|---|---|
| Title: | ICDXML: enhancing ICD coding with probabilistic label trees and dynamic semantic representations |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1038/s41598-024-69214-9 |
| Publisher version: | https://doi.org/10.1038/s41598-024-69214-9 |
| Language: | English |
| Additional information: | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| Keywords: | Science & Technology, Multidisciplinary Sciences, Science & Technology - Other Topics, Natural language processing, ICD coding, Extreme multi-label classification, Few-shot learning, Medical knowledge representation |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Education UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education > IOE - Social Research Institute |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10204078 |
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