Publication details

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

Authors

ZELINA Petr HALÁMKOVÁ Jana NOVÁČEK Vít

Year of publication 2023
Type Article in Periodical
Magazine / Source IEEE TRANSACTIONS ON NANOBIOSCIENCE
MU Faculty or unit

Faculty of Informatics

Citation
Web https://ieeexplore.ieee.org/abstract/document/10123130
Doi http://dx.doi.org/10.1109/TNB.2023.3275195
Keywords NLP; EHR; Clinical Notes; Information Extraction; Text Classification
Description 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.
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