LeafAI:用于临床队列发现的查询生成器与人类程序员竞争.
Nicholas J Dobbins1,2, Bin Han3, Weipeng Zhou1
1Department of Biomedical Informatics & Medical Education, University of Washington, Seattle, Washington, USA.
概括
一个新的系统LeafAI通过自动生成数据模型不可知查询来增强临床试验招聘. 它在识别符合条件的患者方面明显优于人类程序员,节省了宝贵的研究时间.
科学领域:
- 临床信息学 临床信息学
- 医疗保健中的人工智能
- 生物医学数据科学 生物医学数据科学
背景情况:
- 从临床数据库中准确识别患者对于临床研究至关重要.
- 为复杂的资格标准设计有效的查询需要专门的专业知识.
- 现有的方法往往缺乏数据模型不可知论和先进的推理能力.
研究的目的:
- 开发一个用于生成临床试验资格标准的数据模型不可知查询的系统.
- 将复杂的标准纳入新的逻辑推理.
- 在临床研究中自动化和提高患者识别的效率.
主要方法:
- 使用混合深度学习和基于规则的模块来执行文本处理任务.
- 整合了统一的医学语言系统 (UMLS) 和相关的本体学.
- 开发了一种用于用UMLS概念标记数据库模式元素的新方法.
- 使用真实临床试验数据对人类数据库程序员进行了系统 (LeafAI) 的评估.
主要成果:
- 在8项试验中,LeafAI平均匹配了43%的注册患者,而人类程序员的匹配率为27%.
- LeafAI确定了27225名符合条件的患者,远远超过人类程序员确定的14587名患者.
- 查询生成时间从人类的几个小时减少到LeafAI的几分钟.
结论:
- 叶子AI代表了一个最先进的系统,用于数据模型-无条件推理的查询生成.
- 该系统展示了与经验丰富的人类程序员在患者队列识别方面的性能相匹配或超过的能力.
- 这项技术有可能加速临床试验招聘和简化研究流程.
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