Detailed Information on Publication Record
2023
Extraction, labeling, clustering, and semantic mapping of segments from clinical notes
ZELINA, Petr, Jana HALÁMKOVÁ and Vít NOVÁČEKBasic information
Original name
Extraction, labeling, clustering, and semantic mapping 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
IEEE TRANSACTIONS ON NANOBIOSCIENCE, UNITED STATES, IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2023, 1536-1241
Other information
Language
English
Type of outcome
Článek v odborném periodiku
Field of Study
10201 Computer sciences, information science, bioinformatics
Country of publisher
United States of America
Confidentiality degree
není předmětem státního či obchodního tajemství
References:
Impact factor
Impact factor: 3.900 in 2022
RIV identification code
RIV/00216224:14330/23:00131334
Organization unit
Faculty of Informatics
UT WoS
001082250700011
Keywords in English
NLP; EHR; Clinical Notes; Information Extraction; Text Classification
Tags
Tags
International impact, Reviewed
Změněno: 7/4/2024 23:08, RNDr. Pavel Šmerk, Ph.D.
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 that can be used 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 exclusively 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). Finally, we propose a tool for computer-assisted semantic mapping of segment types to pre-defined ontologies and validate it on a downstream task of category-specific patient similarity. The presented results demonstrate the practical relevance of the proposed approach for building more sophisticated extraction and analytical pipelines deployed on Czech clinical notes.
In Czech
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 that can be used 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 exclusively 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). Finally, we propose a tool for computer-assisted semantic mapping of segment types to pre-defined ontologies and validate it on a downstream task of category-specific patient similarity. 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 |
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| MUNI/G/1763/2020, interní kód MU |
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