Extraction, labeling, clustering, and semantic mapping of segments from clinical notes

ZELINA, Petr, Jana HALÁMKOVÁ a Vít NOVÁČEK. Extraction, labeling, 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. Dostupné z: https://dx.doi.org/10.1109/TNB.2023.3275195.

Základní 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

• 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
• Kód RIV: RIV/00216224:14330/23:00131334
• 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

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 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