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 a Vít NOVÁČEK

Základní údaje

Originální název

Unsupervised extraction, labelling and clustering of segments from clinical notes

Autoři

ZELINA, Petr (203 Česká republika, garant, domácí), Jana HALÁMKOVÁ (203 Česká republika, domácí) a Vít NOVÁČEK (203 Česká republika, domácí)

Vydání

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

Nakladatel

IEEE

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Odkazy

arXiv preprint

Kód RIV

RIV/00216224:14330/22:00127605

Organizační jednotka

Fakulta informatiky

ISBN

978-1-6654-6820-6

DOI

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

Klíčová slova anglicky

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

Štítky

Artificial Intelligence, best1, firank_B, knowledge acquisition, knowledge extraction, machine learning, medical informatics, natural language processing, text mining

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 3. 11. 2023 10:49, Mgr. Petr Zelina

Anotace

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.

Návaznosti

MUNI/A/1339/2022, interní kód MU

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

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

MUNI/G/1763/2020, interní kód MU

Název: AIcope - AI support for Clinical Oncology and Patient Empowerment (Akronym: AIcope)

Investor: Masarykova univerzita, AIcope - AI support for Clinical Oncology and Patient Empowerment, INTERDISCIPLINARY - Mezioborové výzkumné projekty

Citovat

ZELINA, Petr, Jana HALÁMKOVÁ a 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, s. 1362-1368. ISBN 978-1-6654-6820-6. Dostupné z: 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 a 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, s.~1362-1368. ISBN~978-1-6654-6820-6. Dostupné z: https://dx.doi.org/10.1109/BIBM55620.2022.9995229.

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