用大模型和集成学习精准提取药物信息并链接标准编码
INSIGHTBUDDY-AI: Medication Extraction and Entity Linking using Large Language Models and Ensemble Learning
- 结合大模型与堆叠/投票集成，提升药物信息抽取性能
- 在通用和专科领域均优于BERT系列等基线模型
- 支持药物术语映射至SNOMED-CT、BNF等临床标准库
药物信息抽取与挖掘在医疗自然语言处理中具有重要应用价值，尤其在医院场景中用于将药物信息映射到标准临床知识库（如SNOMED-CT、BNF）。本文研究了当前先进大语言模型在药物文本挖掘任务中的表现，涵盖剂量、给药途径、强度及不良反应等属性。同时，探索了堆叠集成（Stack-Ensemble）与投票集成（Voting-Ensemble）两种集成学习方法，以提升单个大模型的性能。实验表明，集成模型在通用与特定领域均优于多个基线模型，包括BERT、RoBERTa、RoBERTa-L、BioBERT、BioClinicalBERT、BioMedRoBERTa、ClinicalBERT和PubMedBERT。最终，构建了实体链接功能，将提取的医学术语映射至SNOMED-CT代码与英国国家处方集（BNF）代码，并进一步关联至《药品与设备词典》（dm+d）和ICD。模型工具包与桌面应用已开源（https://github.com/HECTA-UoM/ensemble-NER）。
原文摘要 · Abstract (English)
Medication Extraction and Mining play an important role in healthcare NLP research due to its practical applications in hospital settings, such as their mapping into standard clinical knowledge bases (SNOMED-CT, BNF, etc.). In this work, we investigate state-of-the-art LLMs in text mining tasks on medications and their related attributes such as dosage, route, strength, and adverse effects. In addition, we explore different ensemble learning methods (\textsc{Stack-Ensemble} and \textsc{Voting-Ensemble}) to augment the model performances from individual LLMs. Our ensemble learning result demonstrated better performances than individually fine-tuned base models BERT, RoBERTa, RoBERTa-L, BioBERT, BioClinicalBERT, BioMedRoBERTa, ClinicalBERT, and PubMedBERT across general and specific domains. Finally, we build up an entity linking function to map extracted medical terminologies into the SNOMED-CT codes and the British National Formulary (BNF) codes, which are further mapped to the Dictionary of Medicines and Devices (dm+d), and ICD. Our model's toolkit and desktop applications are publicly available (at \url{https://github.com/HECTA-UoM/ensemble-NER}).
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