基于机器学习的自然历史研究在罕见疾病的识别:迈向了解疾病发展和结果的一步
Kelly Chen1, Minghui Ao1, Sungrim Moon1
1National Center for Advancing Translational Sciences, Rockville, Maryland 20850, United States.
Journal of rare diseases (Berlin, Germany)
|November 17, 2025
概括
这项研究引入了一种机器学习方法,可以在PubMed.中自动识别自然历史研究 (NHS). 这种方法通过有效收集关键疾病进展数据,加速罕见疾病研究和药物开发.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 罕见疾病研究 罕见疾病研究
背景情况:
- 自然史研究 (NHS) 对罕见疾病研究至关重要,有助于流行率估计和生物标志物识别.
- 系统地识别NHS对于支持药物开发的大规模分析至关重要.
- 目前用于识别NHS的方法通常是手动的,耗时的.
研究的目的:
- 开发和评估基于机器学习的方法,用于从PubMed.com自动识别NHS.
- 为在罕见病药物开发中进行大规模NHS分析奠定基础.
- 为了比较NHS识别的二进制与多类分类的性能.
主要方法:
- 使用NHS手工策划的数据库来训练和评估机器学习和深度学习模型.
- 测试了二进制 (NHS相关与NHS无关) 和多类 (无关,无关,二级,初级) 分类方法.
- 使用PubMedBERT-base-uncased-abstract模型进行分类.
主要成果:
- 二元分类模型在识别NHS时显著优于多类模型.
- 在二进制分类中,PubMedBERT-base-uncased-abstract模型获得了最高的性能 (精度=0.8171,回忆=0.8079,F1=0.8125,AUCPR=0.8768).
- 深度学习模型展示了自动化NHS识别的可行性.
结论:
- 使用深度学习的自动化NHS识别是可行的和有效的.
- 二元分类是一种非常有效的初始策略,用于识别NHS.
- 这种方法可以加速用于NHS分析的数据收集,提高对罕见和常见疾病疾病进展的理解.
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