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

ZELINA, Petr, Jana HALÁMKOVÁ and 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, vol. 22, No 4, p. 781-788. ISSN 1536-1241. Available from: https://dx.doi.org/10.1109/TNB.2023.3275195.

Basic 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

• Original language: English
• Type of outcome: Article in a journal
• Field of Study: 10201 Computer sciences, information science, bioinformatics
• Country of publisher: United States of America
• Confidentiality degree: is not subject to a state or trade secret
• WWW: URL
• Impact factor: Impact factor: 3.900 in 2022
• RIV identification code: RIV/00216224:14330/23:00131334
• Organization unit: Faculty of Informatics
• Doi: http://dx.doi.org/10.1109/TNB.2023.3275195
• UT WoS: 001082250700011
• Keywords in English: NLP; EHR; Clinical Notes; Information Extraction; Text Classification
• Tags: 14110811, Artificial Intelligence, knowledge acquisition, knowledge extraction, machine learning, medical informatics, text mining
• Tags: International impact, Reviewed

Abstract

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.

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

Investor: Masaryk University, Development of data processing techniques to support search, analysis and visualization of large datasets using artificial intelligence

• MUNI/G/1763/2020, interní kód MU: Name: AIcope - AI support for Clinical Oncology and Patient Empowerment (Acronym: AIcope)

Investor: Masaryk University, INTERDISCIPLINARY - Interdisciplinary research projects