Unsupervised extraction, labelling and clustering of segments
from clinical notes

D 2022

Unsupervised extraction, labelling and clustering of segments from clinical notes

ZELINA, Petr, Jana HALÁMKOVÁ and Vít NOVÁČEK

Basic information

Original name

Unsupervised extraction, labelling and clustering of segments from clinical notes

Authors

ZELINA, Petr (203 Czech Republic, guarantor, belonging to the institution), Jana HALÁMKOVÁ (203 Czech Republic, belonging to the institution) and Vít NOVÁČEK (203 Czech Republic, belonging to the institution)

Edition

USA, Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM), p. 1362-1368, 7 pp. 2022

Publisher

IEEE

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

References:

arXiv preprint

RIV identification code

RIV/00216224:14330/22:00127605

Organization unit

Faculty of Informatics

ISBN

978-1-6654-6820-6

DOI

http://dx.doi.org/10.1109/BIBM55620.2022.9995229

Keywords in English

NLP; EHR; Clinical Notes; Information Extraction; Text Classification

Abstract

V originále

This work is motivated by the scarcity of tools for accurate, unsupervised information extraction from unstructured clinical notes in computationally underrepresented languages, such as Czech. We introduce a stepping stone to a broad array of downstream tasks such as summarisation or integration of individual patient records, extraction of structured information for national cancer registry reporting or building of semi-structured semantic patient representations for computing patient embeddings. More specifically, we present a method for unsupervised extraction of semantically-labelled textual segments from clinical notes and test it out on a dataset of Czech breast cancer patients, provided by Masaryk Memorial Cancer Institute (the largest Czech hospital specialising in oncology). Our goal was to extract, classify (i.e. label) and cluster segments of the free-text notes that correspond to specific clinical features (e.g., family background, comorbidities or toxicities). The presented results demonstrate the practical relevance of the proposed approach for building more sophisticated extraction and analytical pipelines deployed on Czech clinical notes.

Links

MUNI/A/1339/2022, interní kód MU

Name: Rozvoj technik pro zpracování dat pro podporu vyhledávání, analýz a vizualizací rozsáhlých datových souborů s využitím umělé inteligence

Investor: Masaryk University, Development of data processing techniques to support search, analysis and visualization of large datasets using artificial intelligence

MUNI/G/1763/2020, interní kód MU

Name: AIcope - AI support for Clinical Oncology and Patient Empowerment (Acronym: AIcope)

Investor: Masaryk University, INTERDISCIPLINARY - Interdisciplinary research projects

Citovat

ZELINA, Petr, Jana HALÁMKOVÁ and Vít NOVÁČEK. Unsupervised extraction, labelling and clustering of segments from clinical notes. Online. In Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM). USA: IEEE, 2022, p. 1362-1368. ISBN 978-1-6654-6820-6. Available from: https://dx.doi.org/10.1109/BIBM55620.2022.9995229.

@inproceedings{2242916,
   author = {Zelina, Petr and Halámková, Jana and Nováček, Vít},
   address = {USA},
   booktitle = {Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM)},
   doi = {http://dx.doi.org/10.1109/BIBM55620.2022.9995229},
   keywords = {NLP; EHR; Clinical Notes; Information Extraction; Text Classification},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {USA},
   isbn = {978-1-6654-6820-6},
   pages = {1362-1368},
   publisher = {IEEE},
   title = {Unsupervised extraction, labelling and clustering of segments from clinical notes},
   url = {https://arxiv.org/abs/2211.11799},
   year = {2022}
}

TY  - JOUR
ID  - 2242916
AU  - Zelina, Petr - Halámková, Jana - Nováček, Vít
PY  - 2022
TI  - Unsupervised extraction, labelling and clustering of segments from clinical notes
PB  - IEEE
CY  - USA
SN  - 9781665468206
KW  - NLP
KW  - EHR
KW  - Clinical Notes
KW  - Information Extraction
KW  - Text Classification
UR  - https://arxiv.org/abs/2211.11799
N2  - This work is motivated by the scarcity of tools for accurate, unsupervised information extraction from unstructured clinical notes in computationally underrepresented languages, such as Czech. We introduce a stepping stone to a broad array of downstream tasks such as summarisation or integration of individual patient records, extraction of structured information for national cancer registry reporting or building of semi-structured semantic patient representations for computing patient embeddings. More specifically, we present a method for unsupervised extraction of semantically-labelled textual segments from clinical notes and test it out on a dataset of Czech breast cancer patients, provided by Masaryk Memorial Cancer Institute (the largest Czech hospital specialising in oncology). Our goal was to extract, classify (i.e. label) and cluster segments of the free-text notes that correspond to specific clinical features (e.g., family background, comorbidities or toxicities). The presented results demonstrate the practical relevance of the proposed approach for building more sophisticated extraction and analytical pipelines deployed on Czech clinical notes.
ER  -

ZELINA, Petr, Jana HALÁMKOVÁ and Vít NOVÁČEK. Unsupervised extraction, labelling and clustering of segments from clinical notes. Online. In \textit{Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM)}. USA: IEEE, 2022, p.~1362-1368. ISBN~978-1-6654-6820-6. Available from: https://dx.doi.org/10.1109/BIBM55620.2022.9995229.

Detailed Information on Publication Record