Enhancing and Not Replacing Clinical Expertise: Improving Named-Entity Recognition in Colonoscopy Reports Through Mixed Real-Synthetic Training Sources.

Andrei-Constantin Ioanovici¹, Andrei-Marian Feier², Marius-Ștefan Mărușteri¹, Alina-Dia Trâmbițaș-Miron³, Daniela-Ecaterina Dobru⁴

¹Department M2-Complementary Functional Sciences, Medical Informatics and Biostatistics, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 540142 Targu Mures, Romania.

²Department M4-Clinical Sciences, Orthopedics and Traumatology I, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 540139 Targu Mures, Romania.

³John Snow Labs Inc., 16192 Coastal Highway, Lewes, DE 19958, USA.

⁴Department M4-Clinical Sciences, Gastroenterology Medical VII, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 540139 Targu Mures, Romania.

PubMed PMC DOI PDF

Abstract

Background/Objectives: In routine practice, colonoscopy findings are saved as unstructured free text, limiting secondary use. Accurate named-entity recognition (NER) is essential to unlock these descriptions for quality monitoring, personalized medicine and research. We compared named-entity recognition (NER) models trained on real, synthetic, and mixed data to determine whether privacy preserving…

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