@inproceedings{kim-etal-2022-modeling,
title = "Modeling {D}utch Medical Texts for Detecting Functional Categories and Levels of {COVID}-19 Patients",
author = "Kim, Jenia and
Verkijk, Stella and
Geleijn, Edwin and
van der Leeden, Marieke and
Meskers, Carel and
Meskers, Caroline and
van der Veen, Sabina and
Vossen, Piek and
Widdershoven, Guy",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.488",
pages = "4577--4585",
abstract = "Electronic Health Records contain a lot of information in natural language that is not expressed in the structured clinical data. Especially in the case of new diseases such as COVID-19, this information is crucial to get a better understanding of patient recovery patterns and factors that may play a role in it. However, the language in these records is very different from standard language and generic natural language processing tools cannot easily be applied out-of-the-box. In this paper, we present a fine-tuned Dutch language model specifically developed for the language in these health records that can determine the functional level of patients according to a standard coding framework from the World Health Organization. We provide evidence that our classification performs at a sufficient level to generate patient recovery patterns that can be used in the future to analyse factors that contribute to the rehabilitation of COVID-19 patients and to predict individual patient recovery of functioning.",
}
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<abstract>Electronic Health Records contain a lot of information in natural language that is not expressed in the structured clinical data. Especially in the case of new diseases such as COVID-19, this information is crucial to get a better understanding of patient recovery patterns and factors that may play a role in it. However, the language in these records is very different from standard language and generic natural language processing tools cannot easily be applied out-of-the-box. In this paper, we present a fine-tuned Dutch language model specifically developed for the language in these health records that can determine the functional level of patients according to a standard coding framework from the World Health Organization. We provide evidence that our classification performs at a sufficient level to generate patient recovery patterns that can be used in the future to analyse factors that contribute to the rehabilitation of COVID-19 patients and to predict individual patient recovery of functioning.</abstract>
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%0 Conference Proceedings
%T Modeling Dutch Medical Texts for Detecting Functional Categories and Levels of COVID-19 Patients
%A Kim, Jenia
%A Verkijk, Stella
%A Geleijn, Edwin
%A van der Leeden, Marieke
%A Meskers, Carel
%A Meskers, Caroline
%A van der Veen, Sabina
%A Vossen, Piek
%A Widdershoven, Guy
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F kim-etal-2022-modeling
%X Electronic Health Records contain a lot of information in natural language that is not expressed in the structured clinical data. Especially in the case of new diseases such as COVID-19, this information is crucial to get a better understanding of patient recovery patterns and factors that may play a role in it. However, the language in these records is very different from standard language and generic natural language processing tools cannot easily be applied out-of-the-box. In this paper, we present a fine-tuned Dutch language model specifically developed for the language in these health records that can determine the functional level of patients according to a standard coding framework from the World Health Organization. We provide evidence that our classification performs at a sufficient level to generate patient recovery patterns that can be used in the future to analyse factors that contribute to the rehabilitation of COVID-19 patients and to predict individual patient recovery of functioning.
%U https://aclanthology.org/2022.lrec-1.488
%P 4577-4585
Markdown (Informal)
[Modeling Dutch Medical Texts for Detecting Functional Categories and Levels of COVID-19 Patients](https://aclanthology.org/2022.lrec-1.488) (Kim et al., LREC 2022)
ACL
- Jenia Kim, Stella Verkijk, Edwin Geleijn, Marieke van der Leeden, Carel Meskers, Caroline Meskers, Sabina van der Veen, Piek Vossen, and Guy Widdershoven. 2022. Modeling Dutch Medical Texts for Detecting Functional Categories and Levels of COVID-19 Patients. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 4577–4585, Marseille, France. European Language Resources Association.