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Published on: December 6, 2024
Improving MedDRA/J Coding Accuracy with a Fine-Tuned Text Embedding Model
Shoya Wada1,2, Masaharu Okamoto2, Kento Sugimoto2
1Department of Transformative System for Medical Information, Graduate School of Medicine, The University of Osaka.
Abstract:
MedDRA coding in Japan can be tedious when source expressions differ from dictionary terms. We fine-tune a text embedding model on in-house adverse event (AE) data to enhance search accuracy. Compared to baselines, our model significantly improves MedDRA term ranking and recall (nDCG@20 = 76.2%, Recall@20 = 90.8% at 50K in-house entries). This approach suggests that using real-world AE data with advanced embeddings can greatly benefit MedDRA/J coding.
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