Combining Domain-Specific Models and LLMs for Automated Disease Phenotyping from Survey Data

• URL: http://arxiv.org/abs/2410.20695v1
• Date: Mon, 28 Oct 2024 02:55:03 GMT
• Title: Combining Domain-Specific Models and LLMs for Automated Disease Phenotyping from Survey Data
• Authors: Gal Beeri, Benoit Chamot, Elena Latchem, Shruthi Venkatesh, Sarah Whalan, Van Zyl Kruger, David Martino,
• Abstract summary: This pilot study investigated the potential of combining a domain-specific model, BERN2, with large language models (LLMs) to enhance automated phenotyping from research survey data. We employed BERN2, a named entity recognition and normalization model, to extract information from the ORIGINS survey data. 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.
• Score: 0.0
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• Abstract: 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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