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使用自然语言处理来表征移植评估过程的早期步骤 记录在国家退伍军人事务电子健康记录中
Hannah Eyre1, David K Prince2, Sandra Abrahamson3
1Seattle-Denver Health Systems Research Center of Innovation for Veteran-Centered and Value-Driven Care, Veterans Affairs Puget Sound Health Care System, Seattle, Washington, USA.
Clinical transplantation
|January 14, 2026
概括
许多患有晚期病的患者开始移植评估过程,但很少有人达到等候名单. 自然语言处理 (NLP) 准确地跟踪这些早期步骤在电子健康记录.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 移植手术 移植手术
- 医疗信息学 医疗信息学
背景情况:
- 人口层面的有限数据阻碍了对美国移植评估障碍的理解.
- 移植过程的早期阶段对于患者的准入至关重要,但记录不完善.
研究的目的:
- 用电子健康记录来描述移植评估中的连续步骤.
- 评估自然语言处理 (NLP) 在识别这些评估步骤中的准确性.
主要方法:
- 基于规则的NLP方法分析了来自美国退伍军人事务医疗保健系统 (VA) 电子健康记录 (EHR) 的临床笔记.
- 数据与美国脏数据系统注册处的高级脏病患者 (eGFR ≤20 mL/min/1.73m2) 的数据进行了链接.
- 该研究从2012年至2019年跟踪了45174名患者,结果追踪到2021年.
主要成果:
- 46.3%的患者提到移植作为一种选择; 28.2%的人开始评估.
- 只有8.4%的人被转诊,5.4%的人被评估在VA移植中心.
- 6.9%的人被列入等待名单,3.1%的人接受了移植. 与手动审查相比,NLP显示了高精度 (0.82-0.94) 和回忆 (0.56-0.89).
结论:
- 很大一部分患有晚期病的患者参与了最初的移植评估步骤.
- 在EHR中,NLP是一种经过验证的工具,可以准确识别EHR中的关键移植评估里程碑.
- 这种方法可以改善人口层面的数据收集,以了解移植接入障碍.
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