Unsupervised extraction, labelling and clustering of segments from clinical notes

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Publikace nespadá pod Fakultu sportovních studií, ale pod Fakultu informatiky. Oficiální stránka publikace je na webu muni.cz.
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ZELINA Petr HALÁMKOVÁ Jana NOVÁČEK Vít

Rok publikování 2022
Druh Článek ve sborníku
Konference Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Fakulta / Pracoviště MU

Fakulta informatiky

Citace
www arXiv preprint
Doi http://dx.doi.org/10.1109/BIBM55620.2022.9995229
Klíčová slova NLP; EHR; Clinical Notes; Information Extraction; Text Classification
Popis 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 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 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). 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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