统一的临床词汇嵌入用于推进精准医学
Ruth Johnson1,2, Uri Gottlieb3, Galit Shaham3
1The Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute, Boston, MA, USA.
medRxiv : the preprint server for health sciences
|December 16, 2024
概括
我们从知识图中创建了临床词汇嵌入,以提高AI对医疗数据的理解. 这增强了人工智能.
科学领域:
- 人工智能在医学中的应用
- 生物医学信息学 生物医学信息学
- 临床知识表示 临床知识表示
背景情况:
- 将临床知识整合到人工智能中很困难,因为医疗代码反映的是操作模式,而不是精确的临床信息.
- 这种脱节影响了人工智能代表临床关系的能力,导致偏见,透明度和概括性问题.
研究的目的:
- 开发一种新的临床词汇嵌入的新资源.
- 统一多种医学词汇,以改善人工智能集成.
- 解决AI模型中代表临床知识的差距.
主要方法:
- 构建了一个具有67,124个嵌入点和超过130万个边缘的临床知识图.
- 利用图形转换器神经网络从七个统一的医疗词汇中生成嵌入.
- 通过对457万名患者的表型风险评分分析和90种疾病和3000个代码的临床医生评估验证了嵌入.
主要成果:
- 产生了强大的和可转移的临床词汇嵌入.
- 成功分层患者基于生存结果,使用表型风险得分.
- 临床医师小组证实了对90种疾病和3000个代码的临床知识的一致性.
结论:
- 开发的嵌入式为AI提供了临床知识的新表征.
- 该资源有助于将临床词汇整合到AI模型和训练数据集中.
- 能够开发基于知识的人口和患者级人工智能模型.
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