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通过自适应性大语言模型构建生物医学信息集成.

Xingsi Xue, Mu-En Wu, Fazlullah Khan

IEEE journal of biomedical and health informatics

|November 11, 2024

概括

本研究介绍了生物医学信息整合的双阶段LLM构建 (TSLLM) 框架. TSLLM可自适应地选择和组合大型语言模型 (LLM),以提高生物医学实体对齐和数据集成的准确性.

科学领域:

生物医学信息学生物医学信息学
人工智能的人工智能
数据科学数据科学数据科学

背景情况:

生物医学信息整合 (BII) 对医学进步至关重要,但由于数据异质性而面临挑战.
准确的生物医学实体对齐对于有效的BII至关重要.
现有的大型语言模型 (LLM) 在捕获生物医学数据的全部复杂性以进行实体匹配方面存在局限性.

研究的目的:

提出一个新的两阶段法学士建设 (TSLLM) 框架,用于在BII中适应性选择和结合法学士.
提高生物医学实体对齐的准确性和效率.
提高在区分异构的生物医学实体的歧视力.

主要方法:

开发了一种多目标遗传编程 (MOGP) 算法,用于生成多功能高级LLMs.
实施了一个单一目标遗传算法 (SOGA) 基于信任的战略,将LLMs结合起来.
在OAEI数据集 (基准,会议) 和专业数据集 (LargeBio,疾病,表型) 上评估了TSLLM.

主要成果:

TSLLM在适应性差异化异质生物医学实体方面表现出显著的效率.
与领先的实体匹配技术相比,拟议的框架实现了更高的性能.

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实验结果验证了适应性LLM选择和组合策略的有效性.

结论:

该TSLLM框架提供了一个强大的解决方案,以克服生物医学信息整合的挑战.
适应性LLM组合可以提高生物医学实体对齐的准确性.
通过更好的数据集成,TSLLM代表了利用人工智能的重大进步,以改善患者的治疗结果.