Improving the Quality of Unstructured Cancer Data Using Large Language Models: A German Oncological Case Study.
Stud Health Technol Inform
; 316: 685-689, 2024 Aug 22.
Article
em En
| MEDLINE
| ID: mdl-39176835
ABSTRACT
With cancer being a leading cause of death globally, epidemiological and clinical cancer registration is paramount for enhancing oncological care and facilitating scientific research. However, the heterogeneous landscape of medical data presents significant challenges to the current manual process of tumor documentation. This paper explores the potential of Large Language Models (LLMs) for transforming unstructured medical reports into the structured format mandated by the German Basic Oncology Dataset. Our findings indicate that integrating LLMs into existing hospital data management systems or cancer registries can significantly enhance the quality and completeness of cancer data collection - a vital component for diagnosing and treating cancer and improving the effectiveness and benefits of therapies. This work contributes to the broader discussion on the potential of artificial intelligence or LLMs to revolutionize medical data processing and reporting in general and cancer care in particular.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Processamento de Linguagem Natural
/
Registros Eletrônicos de Saúde
/
Neoplasias
Limite:
Humans
País/Região como assunto:
Europa
Idioma:
En
Revista:
Stud Health Technol Inform
/
Stud. health technol. inform.
/
Studies in health technology and informatics (Online)
Assunto da revista:
INFORMATICA MEDICA
/
PESQUISA EM SERVICOS DE SAUDE
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Alemanha
País de publicação:
Holanda