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创建高有效性真实世界的喘证据的先进方法
Karynsa Kilpatrick1, Katherine Cahill2, Urmila Chandran3
1From the Center for Observational Research, Amgen Inc., Thousand Oaks, CA.
Epidemiology (Cambridge, Mass.)
|November 26, 2024
概括
先进的人工智能 (AI) 方法显著提高了与传统方法相比,喘研究的真实世界证据的准确性. 这些人工智能技术提高了数据质量,从电子健康记录中获得更强大的临床见解.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 现实世界的证据生成
背景情况:
- 喘是一种复杂的疾病,需要高质量的数据来获得强有力的真实世界证据 (RWE).
- 量化数据质量测量对于关键研究变量至关重要.
- 这项研究将传统的RWE方法与使用电子健康记录 (EHR) 数据的AI驱动方法进行比较.
研究的目的:
- 为了比较喘特征提取在传统结构化EHR数据分析和应用到非结构化临床数据的先进AI技术之间的准确性.
- 评估人工智能对RWE在喘研究中的质量和有效性的影响.
主要方法:
- 从3481名患者的6037次医疗保健接触中提取了18个喘相关特征.
- 通过图表抽象与双注释器审查建立了手册参考标准.
- 通过使用科恩的卡帕和使用F1得分的准确度来评估评级者之间的可靠性.
主要成果:
- 传统方法的平均F1得分为52.2%.
- 先进的人工智能方法的平均F1得分为94.7%,相对增加了81.4%.
- 评级者之间的可靠性为0.80 (科恩的卡帕),表明可信的参考标准.
结论:
- 先进的AI方法显著提高了喘研究现实世界的数据质量.
- 在常规收集的医疗保健数据上,可以使用人工智能生成高质量的RWE,包括细粒度的临床特征.
- 数据质量测量是支持高有效度RWE生成的关键.
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