一个系统的时间提取管道用于临床笔记中的医学概念
Deahan Yu1, Ryan W Stidham1, V G Vinod Vydiswaran1
1University of Michigan, Ann Arbor, MI, USA.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|January 15, 2024
概括
本研究介绍了NLP注释-放松-生成 (NRG) 框架,以使用电子健康记录跟踪医疗概念随时间变化的变化. NRG提供了对个体患者和人群的纵向临床特征变化的洞察力.
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.
- 临床数据分析 临床数据分析
背景情况:
- 自然语言处理 (NLP) 越来越多地用于从电子健康记录 (EHR) 中提取临床特征.
- 时间信息对于解释医学概念及其在临床笔记中的随时间变化至关重要.
- 了解患者状况的纵向变化对于有效的医疗保健至关重要.
研究的目的:
- 引入一个系统的框架,NLP注释-放松-生成 (NRG),用于分析来自EHR的医疗概念的时间变化.
- 证明NRG管道在跟踪炎症性肠病患者特定临床特征变化的有用性.
主要方法:
- 该NRG框架通过整合来自多个临床笔记的状态注释和时间来编译医疗概念的变化.
- 该管道用于分析炎症性肠病患者的"肠外表现"和"药物"的变化.
主要成果:
- 随着时间的推移,NRG管道成功地识别和编译了医疗概念变化的事件.
- 该框架证明了它能够提供对临床特征纵向演变的洞察力.
- 在炎症性肠道疾病队列中,追踪特定概念的变化被证明是有效的.
结论:
- NLP注释-放松-生成 (NRG) 框架对于分析电子健康记录中的医学概念的时间动态是有效的.
- NRG提供了对个人和人口水平临床特征的纵向变化有价值的见解.
- 这种方法增强了对随着时间的推移疾病进展和治疗模式的理解.
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