Allen Institute for AI, Seattle, USA.
Faculty of Biomedical Engineering, Technion, Haifa, Israel.
Sci Rep. 2024 Jul 13;14(1):16190. doi: 10.1038/s41598-024-65645-6.
Differential diagnosis is a crucial aspect of medical practice, as it guides clinicians to accurate diagnoses and effective treatment plans. Traditional resources, such as medical books and services like UpToDate, are constrained by manual curation, potentially missing out on novel or less common findings. This paper introduces and analyzes two novel methods to mine etiologies from scientific literature. The first method employs a traditional Natural Language Processing (NLP) approach based on syntactic patterns. By using a novel application of human-guided pattern bootstrapping patterns are derived quickly, and symptom etiologies are extracted with significant coverage. The second method utilizes generative models, specifically GPT-4, coupled with a fact verification pipeline, marking a pioneering application of generative techniques in etiology extraction. Analyzing this second method shows that while it is highly precise, it offers lesser coverage compared to the syntactic approach. Importantly, combining both methodologies yields synergistic outcomes, enhancing the depth and reliability of etiology mining.
鉴别诊断是医学实践中的一个关键环节，因为它指导临床医生做出准确的诊断和有效的治疗计划。传统资源，如医学书籍和 UpToDate 等服务，受到人工编辑的限制，可能会错过新颖或不太常见的发现。本文介绍并分析了两种从科学文献中挖掘病因的新方法。第一种方法采用基于句法模式的传统自然语言处理（NLP）方法。通过新颖的人工引导模式自举应用，可以快速得出模式，并以显著的覆盖率提取症状病因。第二种方法利用生成模型，特别是 GPT-4，并结合事实验证管道，这标志着生成技术在病因提取中的开创性应用。分析第二种方法表明，虽然它具有很高的准确性，但与句法方法相比，它的覆盖范围较小。重要的是，结合这两种方法可以产生协同效果，增强病因挖掘的深度和可靠性。
