| Title |
Combining a large language model with traditional software engineering tools for automated RES-Q registry variable extraction |
| Authors |
Kaubrytė, Gertrūda ; Ulytė, Agnė ; Klimovič, Rytis ; Maslovas, Andrius ; Mikulík, Robert ; Jatužis, Dalius ; Masiliūnas, Rytis |
| DOI |
10.1093/esj/aakag023.1020 |
| Full Text |
|
| Is Part of |
European stroke journal.. Oxford : Oxford University Press. 2026, vol. 11, iss. S1, art. no. ESOC2026A2214, p. i575-i576.. ISSN 2396-9873. eISSN 2396-9881 |
| Abstract [eng] |
Despite substantial advances in digital health data infrastructure, quality improvement efforts still rely on resource-intensive manual data collection. Using a combination of a large language model (LLM) and conventional programming tools, we developed and evaluated the accuracy of a pilot algorithm that enables automated data extraction of the international Registry of Stroke Care Quality (RES-Q) variables. |
| Published |
Oxford : Oxford University Press |
| Type |
Conference paper |
| Language |
English |
| Publication date |
2026 |
| CC license |
|
