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LLM-generated data boosts non-English clinical coding search

By PulseAugur Editorial · [1 sources] ·

Researchers explored the effectiveness of generalistic versus specific embeddings for semantic search in clinical coding across non-English languages. They found that fine-tuning a Spanish biomedical encoder with LLM-generated synthetic data significantly improved performance in languages like Spanish, Catalan, French, and Portuguese. This approach, involving a bi-encoder and a cross-encoder reranker, even surpassed existing English-based models on certain metrics without English biomedical pretraining. AI

IMPACT Demonstrates a method for improving non-English language model performance in specialized domains using synthetic data.

RANK_REASON Academic paper detailing an empirical study on embedding models for clinical coding search. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM-generated data boosts non-English clinical coding search

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Academic paper detailing an empirical study on embedding models for clinical coding search. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · David Rey-Blanco, Roberto Cruz ·

    Generalistic or Specific Embeddings, Which is Better? An Empirical Study on Search for Clinical Coding in Non-English Languages

    arXiv:2605.30529v1 Announce Type: cross Abstract: Sentence-embedding models for semantic search are overwhelmingly developed and evaluated on English corpora. When applied to clinical retrieval in other languages -- particularly retrieval of ICD-10-CM / CIE-10 codes -- recall deg…

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