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.

翻译：本探索性先导研究调查了结合领域特定模型BERN2与大型语言模型（LLM）以增强研究调查数据中自动化疾病表型提取的潜力。研究动机源于对高效准确方法的需求，以协调日益增长的调查数据与标准化疾病本体。我们采用生物医学命名实体识别与规范化模型BERN2，从ORIGINS出生队列调查数据中提取疾病信息。在基于人工标注基准数据集严格评估BERN2性能后，我们通过提示工程、检索增强生成（RAG）和指令微调（IFT）整合了多种LLM以优化模型输出。BERN2在疾病提及的提取与规范化方面表现出高性能，而LLM的集成——特别是通过少样本推理与RAG编排——进一步提升了准确性。该方法，尤其是在融入结构化示例、逻辑推理提示和详细上下文时，为开发高效队列分析和大型异构研究数据集间数据协调工具提供了一条前景广阔的途径。