PDF(Pubmed)
Abstract：
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.
摘要：
鉴别诊断是医学实践的一个重要方面，因为它指导临床医生准确的诊断和有效的治疗计划。传统资源,例如医疗书籍和UpToDate等服务，受到人工策展的约束，可能错过了新的或不太常见的发现。本文介绍和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。通过使用人工引导模式的新应用，快速导出了自举模式，和症状病因被提取，具有显著的覆盖面。第二种方法利用生成模型，特别是GPT-4，加上事实验证管道，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，虽然它非常精确，与句法方法相比，它提供的覆盖范围较小。重要的是,将这两种方法结合起来会产生协同效果，提高病因挖掘的深度和可靠性。
