Szczegóły publikacji
Opis bibliograficzny
Automated symptom-disease association : discovery from clinical notes / Paulina Gacek // W: Progress in Polish artificial intelligence research 6 [Dokument elektroniczny] : 6th Polish Conference on Artifical Intelligence (PP-RAI'2025) : 07–09.04.2025, Katowice, Poland / ed. by Rafał Doroz, Beata Zielosko. — Wersja do Windows. — Dane tekstowe. — Katowice : Wydawnictwo Uniwersytetu Śląskiego, 2025. — e-ISBN: 978-83-226-4405-8. — S. 50–56. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 56, Abstr.
Autor
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 165793 |
|---|---|
| Data dodania do BaDAP | 2026-02-03 |
| Tekst źródłowy | URL |
| Rok publikacji | 2025 |
| Typ publikacji | fragment monografii pokonferencyjnej |
| Otwarty dostęp |  |
| Creative Commons | |
| Wydawca | Uniwersytet Śląski w Katowicach |
Abstract
Clinical notes are a rich source of medical information, but theirunstructured nature limits their utilization in research and clinical decision-making. Transforming this data into a structured format enhances informa-tion retrieval and enables the discovery of meaningful symptom-disease associations. This study proposes an automated knowledge extraction frame-work that leverages large language models (LLMs) to analyze unstructuredclinical notes and construct a patient-centered knowledge graph. Extractedentities are linked to standardized medical ontologies, facilitating efficientquerying and trend analysis. The system is evaluated on real-world clinicaldata, demonstrating its effectiveness in capturing clinically relevant symptom patterns.
