Text Classification Model Explainability for Keyword Extraction-Towards Keyword-Based Summarization of Nursing Care Episodes

Akseli Reunamo; Laura Maria Peltonen; Reetta Mustonen; Minttu Saari; Tapio Salakoski; Sanna Salanterä; Hans Moen

doi:10.3233/SHTI220154

Text Classification Model Explainability for Keyword Extraction-Towards Keyword-Based Summarization of Nursing Care Episodes

Akseli Reunamo, Laura Maria Peltonen, Reetta Mustonen, Minttu Saari, Tapio Salakoski, Sanna Salanterä, Hans Moen

Not published at Aalto University

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

5 Citations (Scopus)

Abstract

Tools to automate the summarization of nursing entries in electronic health records (EHR) have the potential to support healthcare professionals to obtain a rapid overview of a patient's situation when time is limited. This study explores a keyword-based text summarization method for the nursing text that is based on machine learning model explainability for text classification models. This study aims to extract keywords and phrases that provide an intuitive overview of the content in multiple nursing entries in EHRs written during individual patients' care episodes. The proposed keyword extraction method is used to generate keyword summaries from 40 patients' care episodes and its performance is compared to a baseline method based on word embeddings combined with the PageRank method. The two methods were assessed with manual evaluation by three domain experts. The results indicate that it is possible to generate representative keyword summaries from nursing entries in EHRs and our method outperformed the baseline method.

Original language	English
Title of host publication	MEDINFO 2021
Subtitle of host publication	One World, One Health - Global Partnership for Digital Innovation - Proceedings of the 18th World Congress on Medical and Health Informatics
Editors	Paula Otero, Philip Scott, Susan Z. Martin, Elaine Huesing
Publisher	IOS Press
Pages	632-636
Number of pages	5
ISBN (Electronic)	978-1-64368-264-8
DOIs	https://doi.org/10.3233/SHTI220154
Publication status	Published - 6 Jun 2022
MoE publication type	A4 Conference publication
Event	World Congress on Medical and Health Informatics - Virtual, Online Duration: 2 Oct 2021 → 4 Oct 2021 Conference number: 18

Publication series

Name	Studies in Health Technology and Informatics
Publisher	IOS Press
Volume	290
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	World Congress on Medical and Health Informatics
Abbreviated title	MEDINFO
City	Virtual, Online
Period	02/10/2021 → 04/10/2021

Keywords

Electronic Health Records
Natural Language Processing
Nursing

Access to Document

10.3233/SHTI220154

Cite this

Reunamo, A., Peltonen, L. M., Mustonen, R., Saari, M., Salakoski, T., Salanterä, S., & Moen, H. (2022). Text Classification Model Explainability for Keyword Extraction-Towards Keyword-Based Summarization of Nursing Care Episodes. In P. Otero, P. Scott, S. Z. Martin, & E. Huesing (Eds.), MEDINFO 2021: One World, One Health - Global Partnership for Digital Innovation - Proceedings of the 18th World Congress on Medical and Health Informatics (pp. 632-636). (Studies in Health Technology and Informatics; Vol. 290). IOS Press. https://doi.org/10.3233/SHTI220154

Reunamo, Akseli ; Peltonen, Laura Maria ; Mustonen, Reetta et al. / Text Classification Model Explainability for Keyword Extraction-Towards Keyword-Based Summarization of Nursing Care Episodes. MEDINFO 2021: One World, One Health - Global Partnership for Digital Innovation - Proceedings of the 18th World Congress on Medical and Health Informatics. editor / Paula Otero ; Philip Scott ; Susan Z. Martin ; Elaine Huesing. IOS Press, 2022. pp. 632-636 (Studies in Health Technology and Informatics).

@inproceedings{f7f7c12ea9924048b90f0096ce9b759f,

title = "Text Classification Model Explainability for Keyword Extraction-Towards Keyword-Based Summarization of Nursing Care Episodes",

abstract = "Tools to automate the summarization of nursing entries in electronic health records (EHR) have the potential to support healthcare professionals to obtain a rapid overview of a patient's situation when time is limited. This study explores a keyword-based text summarization method for the nursing text that is based on machine learning model explainability for text classification models. This study aims to extract keywords and phrases that provide an intuitive overview of the content in multiple nursing entries in EHRs written during individual patients' care episodes. The proposed keyword extraction method is used to generate keyword summaries from 40 patients' care episodes and its performance is compared to a baseline method based on word embeddings combined with the PageRank method. The two methods were assessed with manual evaluation by three domain experts. The results indicate that it is possible to generate representative keyword summaries from nursing entries in EHRs and our method outperformed the baseline method.",

keywords = "Electronic Health Records, Natural Language Processing, Nursing",

author = "Akseli Reunamo and Peltonen, {Laura Maria} and Reetta Mustonen and Minttu Saari and Tapio Salakoski and Sanna Salanter{\"a} and Hans Moen",

note = "Funding Information: The study had an ethical review and hospital administrative approval. The authors state no conflict of interest. The research was supported by the Academy of Finland (315376). Publisher Copyright: {\textcopyright} 2022 International Medical Informatics Association (IMIA) and IOS Press.; World Congress on Medical and Health Informatics, MEDINFO ; Conference date: 02-10-2021 Through 04-10-2021",

year = "2022",

month = jun,

day = "6",

doi = "10.3233/SHTI220154",

language = "English",

series = "Studies in Health Technology and Informatics",

publisher = "IOS Press",

pages = "632--636",

editor = "Paula Otero and Philip Scott and Martin, {Susan Z.} and Elaine Huesing",

booktitle = "MEDINFO 2021",

address = "Netherlands",

}

Reunamo, A, Peltonen, LM, Mustonen, R, Saari, M, Salakoski, T, Salanterä, S & Moen, H 2022, Text Classification Model Explainability for Keyword Extraction-Towards Keyword-Based Summarization of Nursing Care Episodes. in P Otero, P Scott, SZ Martin & E Huesing (eds), MEDINFO 2021: One World, One Health - Global Partnership for Digital Innovation - Proceedings of the 18th World Congress on Medical and Health Informatics. Studies in Health Technology and Informatics, vol. 290, IOS Press, pp. 632-636, World Congress on Medical and Health Informatics, Virtual, Online, 02/10/2021. https://doi.org/10.3233/SHTI220154

Text Classification Model Explainability for Keyword Extraction-Towards Keyword-Based Summarization of Nursing Care Episodes. / Reunamo, Akseli; Peltonen, Laura Maria; Mustonen, Reetta et al.
MEDINFO 2021: One World, One Health - Global Partnership for Digital Innovation - Proceedings of the 18th World Congress on Medical and Health Informatics. ed. / Paula Otero; Philip Scott; Susan Z. Martin; Elaine Huesing. IOS Press, 2022. p. 632-636 (Studies in Health Technology and Informatics; Vol. 290).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Text Classification Model Explainability for Keyword Extraction-Towards Keyword-Based Summarization of Nursing Care Episodes

AU - Reunamo, Akseli

AU - Peltonen, Laura Maria

AU - Mustonen, Reetta

AU - Saari, Minttu

AU - Salakoski, Tapio

AU - Salanterä, Sanna

AU - Moen, Hans

N1 - Conference code: 18

PY - 2022/6/6

Y1 - 2022/6/6

N2 - Tools to automate the summarization of nursing entries in electronic health records (EHR) have the potential to support healthcare professionals to obtain a rapid overview of a patient's situation when time is limited. This study explores a keyword-based text summarization method for the nursing text that is based on machine learning model explainability for text classification models. This study aims to extract keywords and phrases that provide an intuitive overview of the content in multiple nursing entries in EHRs written during individual patients' care episodes. The proposed keyword extraction method is used to generate keyword summaries from 40 patients' care episodes and its performance is compared to a baseline method based on word embeddings combined with the PageRank method. The two methods were assessed with manual evaluation by three domain experts. The results indicate that it is possible to generate representative keyword summaries from nursing entries in EHRs and our method outperformed the baseline method.

AB - Tools to automate the summarization of nursing entries in electronic health records (EHR) have the potential to support healthcare professionals to obtain a rapid overview of a patient's situation when time is limited. This study explores a keyword-based text summarization method for the nursing text that is based on machine learning model explainability for text classification models. This study aims to extract keywords and phrases that provide an intuitive overview of the content in multiple nursing entries in EHRs written during individual patients' care episodes. The proposed keyword extraction method is used to generate keyword summaries from 40 patients' care episodes and its performance is compared to a baseline method based on word embeddings combined with the PageRank method. The two methods were assessed with manual evaluation by three domain experts. The results indicate that it is possible to generate representative keyword summaries from nursing entries in EHRs and our method outperformed the baseline method.

KW - Electronic Health Records

KW - Natural Language Processing

KW - Nursing

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DO - 10.3233/SHTI220154

M3 - Conference article in proceedings

C2 - 35673093

AN - SCOPUS:85131465443

T3 - Studies in Health Technology and Informatics

SP - 632

EP - 636

BT - MEDINFO 2021

A2 - Otero, Paula

A2 - Scott, Philip

A2 - Martin, Susan Z.

A2 - Huesing, Elaine

PB - IOS Press

T2 - World Congress on Medical and Health Informatics

Y2 - 2 October 2021 through 4 October 2021

ER -

Reunamo A, Peltonen LM, Mustonen R, Saari M, Salakoski T, Salanterä S et al. Text Classification Model Explainability for Keyword Extraction-Towards Keyword-Based Summarization of Nursing Care Episodes. In Otero P, Scott P, Martin SZ, Huesing E, editors, MEDINFO 2021: One World, One Health - Global Partnership for Digital Innovation - Proceedings of the 18th World Congress on Medical and Health Informatics. IOS Press. 2022. p. 632-636. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI220154

Text Classification Model Explainability for Keyword Extraction-Towards Keyword-Based Summarization of Nursing Care Episodes

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