Improving the Quality of Unstructured Cancer Data Using Large Language Models: A German Oncological Case Study...Medical Informatics Europe (MIE) 34th Conference, August 25–29, 2024, Athens, Greece.
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 o...
| Publicado en: | Studies in Health Technology & Informatics Vol. 316; pp. 685 - 690 |
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| Autores principales: | , , , , , , , , |
| Formato: | proceedings tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=179286339&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179286339 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2024 vid: 316 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 179286339 179286339 179286339 10.3233/SHTI240507 179286339 ppf: 685 ppct: 5 formats: tig: atl: Improving the Quality of Unstructured Cancer Data Using Large Language Models: A German Oncological Case Study...Medical Informatics Europe (MIE) 34th Conference, August 25–29, 2024, Athens, Greece. aug: au: Yongli MOU LEHMKUHL, Jonathan SAUERBRUNN, Nicolas KÖCHEL, Anja PANSE, Jens TRUH, Daniel SOWE, Sulayman BRÜMMENDORF, Tim DECKER, Stefan affil: Chair of Computer Science 5, RWTH Aachen University, Germany. sug: subj: Oncology Health Information Systems Registries, Disease Artificial Intelligence Natural Language Processing Neoplasms Therapy Quality Improvement Data Quality Documentation Congresses and Conferences Greece Electronic Health Records Clinical Documentation Improvement Data Management Greece Cost Effectiveness Analysis Case Studies ab: 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. pubtype: Academic Journal doctype: proceedings tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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