ZELINA, Petr, Jana HALÁMKOVÁ a Vít NOVÁČEK. Extraction, labelling, clustering, and semantic mapping of segments from clinical notes. IEEE TRANSACTIONS ON NANOBIOSCIENCE. UNITED STATES: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2023, roč. 22, č. 4, s. 781-788. ISSN 1536-1241. doi:10.1109/TNB.2023.3275195.
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Základní údaje
Originální název Extraction, labelling, 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
Originální 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
WWW URL
Impakt faktor Impact factor: 3.900 v roce 2022
Organizační jednotka Fakulta informatiky
Doi http://dx.doi.org/10.1109/TNB.2023.3275195
UT WoS 001082250700011
Klíčová slova anglicky NLP; EHR; Clinical Notes; Information Extraction; Text Classification
Štítky 14110811, Artificial Intelligence, knowledge acquisition, knowledge extraction, machine learning, medical informatics, text mining
Příznaky Mezinárodní význam, Recenzováno
Změnil Změnila: Mgr. Tereza Miškechová, učo 341652. Změněno: 27. 2. 2024 13:54.
Anotace
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.
Anotace 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 MUNázev: 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 MUNázev: 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
VytisknoutZobrazeno: 5. 3. 2024 11:20