Enhancing Thyroid Pathology With Artificial Intelligence: Automated Data Extraction From Electronic Health Reports Using RUBY
Our study successfully implemented a CNN-based natural language processing (NLP) approach for extracting and structuring data from various EHRs in thyroid pathology. This highlights the potential of artificial intelligence-driven NLP techniques for extensive and cost-effective data extraction, paving the way for creating comprehensive, hospital-wide data warehouses.
Authors
- Alexandre Bozec (ORCID: https://orcid.org/0000-0002-8576-3052)
- Eva Seutin
- Sara Contu (ORCID: https://orcid.org/0000-0001-9235-9675)
- Emmanuel Chamorey (ORCID: https://orcid.org/0000-0002-6841-4571)
- Tanguy Pacé-Loscos (ORCID: https://orcid.org/0009-0007-8723-2507)
- Renaud Schiappa (ORCID: https://orcid.org/0000-0001-5104-9507)
- Agathe Villarmé
- Dorian Culié (ORCID: https://orcid.org/0000-0002-7964-8488)
- Gilles Poissonnet (ORCID: https://orcid.org/0000-0002-9022-4352)
Institutions
- Centre Antoine Lacassagne (FR)
Publication Details
- Journal
- JCO Clinical Cancer Informatics
- Published
- 2024-12-01
- DOI
- https://doi.org/10.1200/cci.23.00263
- Citations
- 1
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.23