@inproceedings{ng-etal-2023-modelling,
title = "Modelling Temporal Document Sequences for Clinical {ICD} Coding",
author = "Ng, Boon Liang Clarence and
Santos, Diogo and
Rei, Marek",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-main.120",
doi = "10.18653/v1/2023.eacl-main.120",
pages = "1640--1649",
abstract = "Past studies on the ICD coding problem focus on predicting clinical codes primarily based on the discharge summary. This covers only a small fraction of the notes generated during each hospital stay and leaves potential for improving performance by analysing all the available clinical notes. We propose a hierarchical transformer architecture that uses text across the entire sequence of clinical notes in each hospital stay for ICD coding, and incorporates embeddings for text metadata such as their position, time, and type of note. While using all clinical notes increases the quantity of data substantially, superconvergence can be used to reduce training costs. We evaluate the model on the MIMIC-III dataset. Our model exceeds the prior state-of-the-art when using only discharge summaries as input, and achieves further performance improvements when all clinical notes are used as input.",
}
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%0 Conference Proceedings
%T Modelling Temporal Document Sequences for Clinical ICD Coding
%A Ng, Boon Liang Clarence
%A Santos, Diogo
%A Rei, Marek
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F ng-etal-2023-modelling
%X Past studies on the ICD coding problem focus on predicting clinical codes primarily based on the discharge summary. This covers only a small fraction of the notes generated during each hospital stay and leaves potential for improving performance by analysing all the available clinical notes. We propose a hierarchical transformer architecture that uses text across the entire sequence of clinical notes in each hospital stay for ICD coding, and incorporates embeddings for text metadata such as their position, time, and type of note. While using all clinical notes increases the quantity of data substantially, superconvergence can be used to reduce training costs. We evaluate the model on the MIMIC-III dataset. Our model exceeds the prior state-of-the-art when using only discharge summaries as input, and achieves further performance improvements when all clinical notes are used as input.
%R 10.18653/v1/2023.eacl-main.120
%U https://aclanthology.org/2023.eacl-main.120
%U https://doi.org/10.18653/v1/2023.eacl-main.120
%P 1640-1649
Markdown (Informal)
[Modelling Temporal Document Sequences for Clinical ICD Coding](https://aclanthology.org/2023.eacl-main.120) (Ng et al., EACL 2023)
ACL
- Boon Liang Clarence Ng, Diogo Santos, and Marek Rei. 2023. Modelling Temporal Document Sequences for Clinical ICD Coding. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 1640–1649, Dubrovnik, Croatia. Association for Computational Linguistics.