# Unsupervised extraction, labelling and clustering of segments from clinical notes

arXiv: https://arxiv.org/abs/2211.11799 · HTML: https://mlsanity.com/paper/2211.11799/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 21 Nov 2022.

## Builds on (most cited first, 4 of 4)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Multilingual LAMA: Investigating Knowledge in Multilingual Pretrained Language Models](https://mlsanity.com/paper/2102.00894/) (2021)
- [RobeCzech: Czech RoBERTa, a monolingual contextualized language representation model](https://mlsanity.com/paper/2105.11314/) (2021)

## Built on by (most cited first, 2 of 2)

- [Natural Language Processing for Analyzing Electronic Health Records and Clinical Notes in Cancer Research: A Review](https://mlsanity.com/paper/2410.22180/) (2024)
- [Computing patient similarity based on unstructured clinical notes](https://mlsanity.com/paper/2601.07385/) (2026)

## Abstract (arXiv)

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.