通过实体提取和神经重新排序,有效地将患者与临床试验相匹配
Wojciech Kusa1, Óscar E Mendoza2, Petr Knoth3
1TU Wien, Favoritenstrasse 9-11, Vienna, Austria.
Journal of biomedical informatics
|July 14, 2023
概括
用于临床试验 (CTs) 的自动患者匹配通过使用数据丰富和变压器重新排名方案来改善招聘. 这种方法将试验检索准确度提高15%,克服语义复杂性和资格标准的否定.
科学领域:
- 生物医学信息学 生物医学信息学
- 临床试验招聘 临床试验招聘
- 自然语言处理自然语言处理.
背景情况:
- 临床试验 (CT) 经常面临患者招募方面的挑战,阻碍了研究进展.
- 有效的患者匹配需要将患者数据与复杂的CT资格标准进行比较,通常涉及非结构化文本和否定.
研究的目的:
- 开发和评估一种自动化的方法,以改善患者对临床试验的匹配.
- 解决语义复杂性和否定挑战,使患者信息与CT资格标准相匹配.
主要方法:
- 实现了一个管道模型,用于查询和文档的数据丰富.
- 在患者描述和CT资格标准上使用命名实体识别和否定检测.
- 开发了一个两步的变压器网络培训方案,用于重新排名检索的试验.
主要成果:
- 数据丰富技术显著改善了相关试验的检索.
- 拟议的重新排名策略提高了CT检索性能,精度提高了15%.
- 发现CT的包含标准部分对相关性得分有很大的影响.
结论:
- 提取的实体和拟议的重新排名方案为临床试验检索提供了一个有希望和有效的方法.
- 这种方法甚至在有限的训练数据下也表现出有效性,超过了更大的神经模型.
- 研究结果为推进自动化临床文档检索系统提供了宝贵的见解.
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