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.

本研究解决了如何有效准确地将日益增长的调查数据与标准化疾病本体相结合的问题。通过将专门的生物医学命名实体识别模型BERN2与大型语言模型结合，该研究发现整合后可显著提高疾病信息提取的准确性，尤其是使用少量推理和检索增强生成的方法。此方法为开发能够高效筛选和整合大规模异质研究数据集的工具提供了新途径。

结合特定领域模型和大型语言模型自动化病症表型分析的研究