2023
Extraction, labeling, clustering, and semantic mapping of segments from clinical notes
ZELINA, Petr, Jana HALÁMKOVÁ a Vít NOVÁČEKZákladní údaje
Originální název
Extraction, labeling, clustering, and semantic mapping 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
IEEE TRANSACTIONS ON NANOBIOSCIENCE, UNITED STATES, IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2023, 1536-1241
Další údaje
Jazyk
angličtina
Typ výsledku
Článek v odborném periodiku
Obor
10201 Computer sciences, information science, bioinformatics
Stát vydavatele
Spojené státy
Utajení
není předmětem státního či obchodního tajemství
Odkazy
Impakt faktor
Impact factor: 3.900 v roce 2022
Kód RIV
RIV/00216224:14330/23:00131334
Organizační jednotka
Fakulta informatiky
UT WoS
001082250700011
Klíčová slova anglicky
NLP; EHR; Clinical Notes; Information Extraction; Text Classification
Štítky
Příznaky
Mezinárodní význam, Recenzováno
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.
Česky
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.
Návaznosti
| MUNI/A/1339/2022, interní kód MU |
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| MUNI/G/1763/2020, interní kód MU |
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