Modeling Dutch Medical Texts for Detecting Functional Categories and Levels of COVID-19 Patients

J. Kim; S. Verkijk; P. Vossen; E. Geleijn; M. van der Leeden; C. Meskers; C. Meskers; S. van der Veen; G. Widdershoven

Modeling Dutch Medical Texts for Detecting Functional Categories and Levels of COVID-19 Patients

J. Kim
, S. Verkijk
, P. Vossen
, E. Geleijn
, M. van der Leeden
, C. Meskers
, C. Meskers
, S. van der Veen
, G. Widdershoven

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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 (F1-score above 80% for the main categories and error rates of less than 1 level on a 5-point Likert scale for levels) to generate patient recovery patterns that can be used to analyse factors that contribute to the rehabilitation of COVID-19 patients and to predict individual patient recovery of functioning.

Original language	English
Title of host publication	2022 Language Resources and Evaluation Conference (LREC 2022)
Editors	Nicoletta Calzolari, Frederic Bechet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Helene Mazo, Jan Odijk, Stelios Piperidis
Publisher	European Language Resources Association (ELRA)
Pages	4577-4585
Number of pages	9
ISBN (Electronic)	9791095546726
Publication status	Published - 2022
Event	13th International Conference on Language Resources and Evaluation Conference, LREC 2022 - Marseille, France Duration: 20 Jun 2022 → 25 Jun 2022

Conference

Conference	13th International Conference on Language Resources and Evaluation Conference, LREC 2022
Country/Territory	France
City	Marseille
Period	20/06/22 → 25/06/22

Bibliographical note

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Access to Document

http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.488.pdf

Persistent URL (handle)

Persistent link

Cite this

Kim, J., Verkijk, S., Vossen, P., Geleijn, E., van der Leeden, M., Meskers, C., Meskers, C., van der Veen, S., & Widdershoven, G. (2022). Modeling Dutch Medical Texts for Detecting Functional Categories and Levels of COVID-19 Patients. In N. Calzolari, F. Bechet, P. Blache, K. Choukri, C. Cieri, T. Declerck, S. Goggi, H. Isahara, B. Maegaard, J. Mariani, H. Mazo, J. Odijk, & S. Piperidis (Eds.), 2022 Language Resources and Evaluation Conference (LREC 2022) (pp. 4577-4585). European Language Resources Association (ELRA). http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.488.pdf

Kim, J. ; Verkijk, S. ; Vossen, P. et al. / Modeling Dutch Medical Texts for Detecting Functional Categories and Levels of COVID-19 Patients. 2022 Language Resources and Evaluation Conference (LREC 2022). editor / Nicoletta Calzolari ; Frederic Bechet ; Philippe Blache ; Khalid Choukri ; Christopher Cieri ; Thierry Declerck ; Sara Goggi ; Hitoshi Isahara ; Bente Maegaard ; Joseph Mariani ; Helene Mazo ; Jan Odijk ; Stelios Piperidis. European Language Resources Association (ELRA), 2022. pp. 4577-4585

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title = "Modeling Dutch Medical Texts for Detecting Functional Categories and Levels of COVID-19 Patients",

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 (F1-score above 80\% for the main categories and error rates of less than 1 level on a 5-point Likert scale for levels) to generate patient recovery patterns that can be used to analyse factors that contribute to the rehabilitation of COVID-19 patients and to predict individual patient recovery of functioning.",

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editor = "Nicoletta Calzolari and Frederic Bechet and Philippe Blache and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Helene Mazo and Jan Odijk and Stelios Piperidis",

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Kim, J, Verkijk, S, Vossen, P, Geleijn, E, van der Leeden, M, Meskers, C, Meskers, C, van der Veen, S & Widdershoven, G 2022, Modeling Dutch Medical Texts for Detecting Functional Categories and Levels of COVID-19 Patients. in N Calzolari, F Bechet, P Blache, K Choukri, C Cieri, T Declerck, S Goggi, H Isahara, B Maegaard, J Mariani, H Mazo, J Odijk & S Piperidis (eds), 2022 Language Resources and Evaluation Conference (LREC 2022). European Language Resources Association (ELRA), pp. 4577-4585, 13th International Conference on Language Resources and Evaluation Conference, LREC 2022, Marseille, France, 20/06/22. <http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.488.pdf>

Modeling Dutch Medical Texts for Detecting Functional Categories and Levels of COVID-19 Patients. / Kim, J.; Verkijk, S.; Vossen, P. et al.
2022 Language Resources and Evaluation Conference (LREC 2022). ed. / Nicoletta Calzolari; Frederic Bechet; Philippe Blache; Khalid Choukri; Christopher Cieri; Thierry Declerck; Sara Goggi; Hitoshi Isahara; Bente Maegaard; Joseph Mariani; Helene Mazo; Jan Odijk; Stelios Piperidis. European Language Resources Association (ELRA), 2022. p. 4577-4585.

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Modeling Dutch Medical Texts for Detecting Functional Categories and Levels of COVID-19 Patients

AU - Kim, J.

AU - Verkijk, S.

AU - Vossen, P.

AU - Geleijn, E.

AU - van der Leeden, M.

AU - Meskers, C.

AU - van der Veen, S.

AU - Widdershoven, G.

N1 - © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0

PY - 2022

Y1 - 2022

N2 - 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 (F1-score above 80% for the main categories and error rates of less than 1 level on a 5-point Likert scale for levels) to generate patient recovery patterns that can be used to analyse factors that contribute to the rehabilitation of COVID-19 patients and to predict individual patient recovery of functioning.

AB - 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 (F1-score above 80% for the main categories and error rates of less than 1 level on a 5-point Likert scale for levels) to generate patient recovery patterns that can be used to analyse factors that contribute to the rehabilitation of COVID-19 patients and to predict individual patient recovery of functioning.

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UR - https://www.scopus.com/pages/publications/85144431747#tab=citedBy

M3 - Conference contribution

SP - 4577

EP - 4585

BT - 2022 Language Resources and Evaluation Conference (LREC 2022)

A2 - Calzolari, Nicoletta

A2 - Bechet, Frederic

A2 - Blache, Philippe

A2 - Choukri, Khalid

A2 - Cieri, Christopher

A2 - Declerck, Thierry

A2 - Goggi, Sara

A2 - Isahara, Hitoshi

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PB - European Language Resources Association (ELRA)

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ER -

Kim J, Verkijk S, Vossen P, Geleijn E, van der Leeden M, Meskers C et al. Modeling Dutch Medical Texts for Detecting Functional Categories and Levels of COVID-19 Patients. In Calzolari N, Bechet F, Blache P, Choukri K, Cieri C, Declerck T, Goggi S, Isahara H, Maegaard B, Mariani J, Mazo H, Odijk J, Piperidis S, editors, 2022 Language Resources and Evaluation Conference (LREC 2022). European Language Resources Association (ELRA). 2022. p. 4577-4585 Epub 2022 Jun 20.

Modeling Dutch Medical Texts for Detecting Functional Categories and Levels of COVID-19 Patients

Abstract

Conference

Bibliographical note

UN SDGs

Access to Document

Persistent URL (handle)

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