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相关概念视频

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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
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开发和验证可计算的表型,用于识别酒精使用障碍患者,使用结构化和非结构化EHR数据.

Hao Dai1, Elliot B Tapper2, Lili Zhao3

  • 1Department of Biostatistics & Health Data Science, Indiana University School of Medicine, 410 W 10th St, Indianapolis, IN 46202, United States.

Alcohol and alcoholism (Oxford, Oxfordshire)
|January 8, 2026
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概括
此摘要是机器生成的。

在电子健康记录中精确识别酒精使用障碍 (AUD) 是使用可计算表型 (CP) 改进的. 这些CP整合了多样化的数据,超过了研究和患者护理的传统ICD代码.

关键词:
酒精使用障碍 饮酒障碍可计算的现象型.自然语言处理自然语言处理.现实世界的证据.

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科学领域:

  • 生物医学信息学 生物医学信息学
  • 临床研究信息学

背景情况:

  • 酒精使用障碍 (AUD) 导致显著的发病率,特别是肝脏疾病.
  • 在电子健康记录 (EHR) 中准确的AUD识别对于研究和临床护理至关重要.
  • 现有的国际疾病分类 (ICD) 基于代码的方法在病例识别方面存在局限性,手动审查是不可扩展的.

研究的目的:

  • 开发和验证可计算的表型 (CP) 以使用集成的EHR数据进行准确的AUD识别.
  • 改进传统ICD代码为AUD病例确定的基于算法的局限性.
  • 创建一个可扩展的解决方案,用于识别患有AUD的患者,用于研究,监测和质量改善.

主要方法:

  • 开发了AUD CPs,在一个大规模的EHR数据集 (200万患者) 上使用了两步过程.
  • 使用AUD相关的ICD代码,药物和关键字搜索 (结构化和非结构化数据) 识别了候选队列.
  • 基于规则的算法通过手动图表审查反复改进,并对灵敏度,正预测值 (PPV) 和F1得分进行评估,并对独立数据集进行验证.

主要成果:

  • 优化F1的CP获得了F1得分0.87 (灵敏度:0.98,PPV:0.78).
  • 精度优化的CP实现了0.9的PPV (灵敏度:0.68,F1得分:0.77).
  • 培训方案表现出强度和通用性,培训和测试套件之间的性能差异最小,远远超过仅使用ICD的方法.

结论:

  • 集成结构化和非结构化的电子健康记录数据的可计算表型 (CP) 提供了准确和可重复的AUD识别.
  • 这种方法在性能上超越了传统的基于AUD特定的ICD方法.
  • 临床医生为临床研究,公共卫生监测和AUD的质量改进计划提供了有效的队列建设.