From Examples to Patterns: LLM-Generated Reg-ular Expressions for Entity Extraction in Czech Clinical Texts
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F24%3A00138709" target="_blank" >RIV/00216224:14330/24:00138709 - isvavai.cz</a>
Result on the web
—
DOI - Digital Object Identifier
—
Alternative languages
Result language
angličtina
Original language name
From Examples to Patterns: LLM-Generated Reg-ular Expressions for Entity Extraction in Czech Clinical Texts
Original language description
Entity extraction in clinical texts is essential for converting unstructured data in clinical notes into structured formats, facilitating large-scale analysis and clinical decision support. Traditional methods often rely on handcrafted regular expressions (regexes), which, while effective, demand significant time and specialized knowledge to create -- resources that healthcare professionals may lack. We introduce a novel approach leveraging large language models (LLMs) to automate regex generation for clinical entity extraction. Our method involves prompting LLMs to generate regex patterns from examples, followed by iterative refinement using a feedback loop. Despite regex limitations, this approach is practical for extracting frequently patterned information common in clinical texts, such as dates, specific data about medical procedures or event detection. Our experiments on Czech clinical notes show this method outperforms current SOTA genetic-programming-based methods for generating regular expression patterns from examples, especially when there are few of them.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the Eighteenth Workshop on Recent Advances in Slavonic Natural Languages Processing, RASLAN 2024
ISBN
9788026318354
ISSN
2336-4289
e-ISSN
—
Number of pages
14
Pages from-to
3-16
Publisher name
Tribun EU
Place of publication
Brno
Event location
Brno
Event date
Jan 1, 2024
Type of event by nationality
CST - Celostátní akce
UT code for WoS article
—