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.

Many mainstream OCR techniques involve training a character recognition model using labeled exemplary images of each individual character to be recognized. For modern printed writing, such data can be easily created by automated methods such as rasterizing appropriate font data to produce clean example images. For historical OCR in printing and writing styles distinct from those embodied in modern fonts, appropriate character images must instead be extracted from actual historical documents to achieve good recognition accuracy. For languages with small character sets it may feasible to perform this process manually, but for languages with many thousands of characters, such as Chinese, manually collecting this data is often not practical.