5 citations · 7 across the 3 of their papers we have counts for
3 papers
Computing patient similarity based on unstructured clinical notes
Petr Zelina, Marko Řeháček, Jana Halámková +3
Clinical notes hold rich yet unstructured details about diagnoses, treatments, and outcomes that are vital to precision medicine but hard to exploit at scale. We introduce a method…
Evaluating Open-Weight Large Language Models for Structured Data Extraction from Narrative Medical Reports Across Multiple Use Cases and Languages
Douwe J. Spaanderman, Karthik Prathaban, Petr Zelina +20
Large language models (LLMs) are increasingly used to extract structured information from free-text clinical records, but prior work often focuses on single tasks, limited models,…
Unsupervised extraction, labelling and clustering of segments from clinical notes
Petr Zelina, Jana Halámková, Vít Nováček
This work is motivated by the scarcity of tools for accurate, unsupervised information extraction from unstructured clinical notes in computationally underrepresented languages, su…