通过基于深度学习的信息提取来描述药物批准的文本中识别患者群体:开发自然语言处理算法
Aline Gendrin1, Leonidas Souliotis1, James Loudon-Griffiths1
1AstraZeneca, Cambridge, United Kingdom.
JMIR formative research
|June 22, 2023
概括
我们开发了一个自然语言处理算法来提取瘤药物批准细节,改善了主题专家的信息检索. 这种自动化有助于了解患者群体和临床试验,加速在动态领域的研究.
科学领域:
- 在瘤学瘤学.
- 计算生物学 计算生物学
- 自然语言处理自然语言处理.
背景情况:
- 随着制药发展的快速步伐,跟上新药批准的步伐是具有挑战性的.
- 准确和高效的数据可视化对于了解药物市场格局至关重要.
- 自动信息提取可以通过减少错误和节省时间来显著帮助主题专家.
研究的目的:
- 从瘤药物批准描述中半自动提取疾病人口数据.
- 提取治疗线,癌症阶段和支持6种选择药物标的临床试验.
- 提高毒品信息和相关项目来源的可搜索性.
主要方法:
- 微调特定提取任务的变压器 (BERT) 深度学习模型的双向编码器表示.
- 实施和比较基于规则的文本挖掘方法与深度学习模型.
- 由主题专家手动整理结果,以改进和再培训模型.
主要成果:
- 深度学习模型在分类任务中实现了61% (治疗线) 和56% (癌症阶段) 的准确性.
- 对于试验识别的命名实体认可,F1得分为87%.
- 基于规则的方法对治疗线和癌症阶段的准确率分别为60%和74%.
结论:
- 开发了一种自然语言处理算法,以帮助专家从药物批准中提取关键信息.
- 半自动化系统加速信息检索,并支持瘤学中更深入的分析.
- 这个工具有助于导航药物批准和临床试验数据的复杂性.
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