癌症临床试验的文字分类 资格标准
Yumeng Yang1, Soumya Jayaraj1, Ethan Ludmir2,3
1School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|January 15, 2024
概括
自动化临床试验资格查至关重要. 这项研究表明,文本分类模型,特别是临床试验BERT模型,可以准确地从自然语言描述中确定常见的癌症试验排除标准.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 临床试验 临床试验
背景情况:
- 鉴定患者是否有资格参加临床试验是具有挑战性的,因为标准的自然语言描述.
- 需要自动化方法来简化临床试验招聘流程.
研究的目的:
- 评估使用文本分类来确定癌症试验中常见排除标准的可行性.
- 将标准变压器模型的性能与专门的临床试验BERT模型进行比较.
主要方法:
- 使用了764项III期癌症试验的数据集,并注释了排除标准.
- 常见的排除标准包括先前的恶性瘤,艾滋病毒,乙型/乙型肝炎,精神疾病,药物滥用和自身免疫性疾病.
- 实验包括训练和评估变压器模型和预训练的临床试验BERT模型.
主要成果:
- 文本分类模型证明了自动识别常见排除标准的可行性.
- 预训练的临床试验BERT模型在所有评估标准中实现了最高的平均性能.
- 这凸显了特定领域预先训练的语言模型的好处.
结论:
- 通过使用文本分类来实现临床试验排除标准的自动分类.
- 专门的语言模型,如临床试验BERT,为此任务提供了卓越的性能.
- 这些进展可以显著提高临床试验匹配的效率.
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