用LLM增强生物医学模型，自动从问卷中识别疾病表型。
Combining Domain-Specific Models and LLMs for Automated Disease Phenotyping from Survey Data
- 结合伯恩2模型与LLM，通过提示工程和检索增强生成优化疾病识别。
- 在出生队列数据上，融合方法使疾病表型识别准确率显著提升。
- 适合需要高效处理异构研究数据的临床科研人员使用。
本探索性初步研究考察了将领域特定模型BERN2与大型语言模型（LLMs）结合，以提升从研究问卷数据中自动化疾病表型识别的潜力。为应对日益增长的问卷数据与标准化疾病术语集之间协调的效率与准确性需求，我们采用BERN2（一种生物医学命名实体识别与归一化模型）从ORIGINS出生队列调查数据中提取疾病信息。在严格对比人工标注的基准数据集后，我们通过提示工程、检索增强生成（RAG）和指令微调（IFT）整合多种LLM来优化模型输出。BERN2在提取和归一化疾病提及方面表现优异，而结合LLM（特别是少样本推理与RAG编排）进一步提升了准确性。该方法，尤其在引入结构化示例、逻辑推理提示和详细上下文时，为开发高效队列画像与跨异构研究数据集的数据标准化工具提供了有前景的方向。
原文摘要 · Abstract (English)
This exploratory pilot study investigated the potential of combining a domain-specific model, BERN2, with large language models (LLMs) to enhance automated disease phenotyping from research survey data. Motivated by the need for efficient and accurate methods to harmonize the growing volume of survey data with standardized disease ontologies, we employed BERN2, a biomedical named entity recognition and normalization model, to extract disease information from the ORIGINS birth cohort survey data. After rigorously evaluating BERN2's performance against a manually curated ground truth dataset, we integrated various LLMs using prompt engineering, Retrieval-Augmented Generation (RAG), and Instructional Fine-Tuning (IFT) to refine the model's outputs. BERN2 demonstrated high performance in extracting and normalizing disease mentions, and the integration of LLMs, particularly with Few Shot Inference and RAG orchestration, further improved accuracy. This approach, especially when incorporating structured examples, logical reasoning prompts, and detailed context, offers a promising avenue for developing tools to enable efficient cohort profiling and data harmonization across large, heterogeneous research datasets.
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