@inproceedings{zhang-etal-2020-bert,
title = "{BERT}-{XML}: Large Scale Automated {ICD} Coding Using {BERT} Pretraining",
author = "Zhang, Zachariah and
Liu, Jingshu and
Razavian, Narges",
editor = "Rumshisky, Anna and
Roberts, Kirk and
Bethard, Steven and
Naumann, Tristan",
booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.clinicalnlp-1.3",
doi = "10.18653/v1/2020.clinicalnlp-1.3",
pages = "24--34",
abstract = "ICD coding is the task of classifying and cod-ing all diagnoses, symptoms and proceduresassociated with a patient{'}s visit. The process isoften manual, extremely time-consuming andexpensive for hospitals as clinical interactionsare usually recorded in free text medical notes. In this paper, we propose a machine learningmodel, BERT-XML, for large scale automatedICD coding of EHR notes, utilizing recentlydeveloped unsupervised pretraining that haveachieved state of the art performance on a va-riety of NLP tasks. We train a BERT modelfrom scratch on EHR notes, learning with vo-cabulary better suited for EHR tasks and thusoutperform off-the-shelf models. We furtheradapt the BERT architecture for ICD codingwith multi-label attention. We demonstratethe effectiveness of BERT-based models on thelarge scale ICD code classification task usingmillions of EHR notes to predict thousands ofunique codes.",
}
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<abstract>ICD coding is the task of classifying and cod-ing all diagnoses, symptoms and proceduresassociated with a patient’s visit. The process isoften manual, extremely time-consuming andexpensive for hospitals as clinical interactionsare usually recorded in free text medical notes. In this paper, we propose a machine learningmodel, BERT-XML, for large scale automatedICD coding of EHR notes, utilizing recentlydeveloped unsupervised pretraining that haveachieved state of the art performance on a va-riety of NLP tasks. We train a BERT modelfrom scratch on EHR notes, learning with vo-cabulary better suited for EHR tasks and thusoutperform off-the-shelf models. We furtheradapt the BERT architecture for ICD codingwith multi-label attention. We demonstratethe effectiveness of BERT-based models on thelarge scale ICD code classification task usingmillions of EHR notes to predict thousands ofunique codes.</abstract>
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%0 Conference Proceedings
%T BERT-XML: Large Scale Automated ICD Coding Using BERT Pretraining
%A Zhang, Zachariah
%A Liu, Jingshu
%A Razavian, Narges
%Y Rumshisky, Anna
%Y Roberts, Kirk
%Y Bethard, Steven
%Y Naumann, Tristan
%S Proceedings of the 3rd Clinical Natural Language Processing Workshop
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F zhang-etal-2020-bert
%X ICD coding is the task of classifying and cod-ing all diagnoses, symptoms and proceduresassociated with a patient’s visit. The process isoften manual, extremely time-consuming andexpensive for hospitals as clinical interactionsare usually recorded in free text medical notes. In this paper, we propose a machine learningmodel, BERT-XML, for large scale automatedICD coding of EHR notes, utilizing recentlydeveloped unsupervised pretraining that haveachieved state of the art performance on a va-riety of NLP tasks. We train a BERT modelfrom scratch on EHR notes, learning with vo-cabulary better suited for EHR tasks and thusoutperform off-the-shelf models. We furtheradapt the BERT architecture for ICD codingwith multi-label attention. We demonstratethe effectiveness of BERT-based models on thelarge scale ICD code classification task usingmillions of EHR notes to predict thousands ofunique codes.
%R 10.18653/v1/2020.clinicalnlp-1.3
%U https://aclanthology.org/2020.clinicalnlp-1.3
%U https://doi.org/10.18653/v1/2020.clinicalnlp-1.3
%P 24-34
Markdown (Informal)
[BERT-XML: Large Scale Automated ICD Coding Using BERT Pretraining](https://aclanthology.org/2020.clinicalnlp-1.3) (Zhang et al., ClinicalNLP 2020)
ACL