Unsupervised extraction, labelling and clustering of segments from clinical notes

TL;DR

This paper introduces an unsupervised method for extracting, labeling, and clustering clinical note segments in Czech, enabling better information retrieval and analysis in underrepresented languages.

Contribution

It presents a novel unsupervised approach for semantic segmentation and classification of clinical notes, specifically tailored for Czech language data.

Findings

Effective extraction and labeling of clinical segments demonstrated on Czech breast cancer data.

Improved potential for downstream tasks like summarization and patient record integration.

Practical relevance shown through deployment in a clinical setting.

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 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.