评估疾病表型定义中的算法公平性的通用工具
Jacob S Zelko1, Justin Manjourides2,2
1Department of Mathematics, Northeastern University, Boston, MA, USA.
概括
确保准确的患者群体定义对于可靠的观察性研究至关重要. 这项研究引入了一个工具,以评估疾病定义在不同的人口群体中的公平性,促进公平的研究实践.
科学领域:
- 医疗信息学 医疗信息学
- 流行病学 流行病学
- 生物统计学 生物统计学
背景情况:
- 可靠的观察性研究取决于准确的患者群体定义.
- 在数据集中标准化和实施疾病定义存在重大挑战.
- 确保患者群体中不同的人口动态的公平代表是必要的,以避免过度概括.
研究的目的:
- 为评估疾病定义的公平性提供一个通用的工具.
- 用共同的公平度指标评估疾病定义的实施情况.
- 提供一种可靠的方法来检查各种特征的人口公平性.
主要方法:
- 开发了一个通用的工具来评估疾病定义的公平性.
- 计算公平度指标以评估人口代表性.
- 检查了跨多种人口特征的粗和强烈交叉的种群.
- 使用OMOP常用数据模型 (CDM) 患者数据库进行示例分析.
主要成果:
- 展示了一种评估疾病定义实施中的公平性方法.
- 突出了应用公平性评估工具的实际工作流程.
- 提供了一个示例分析,展示了该工具对现实世界的数据的实用性.
结论:
- 开发的工具提供了一个强大的方法来评估疾病定义的公平性.
- 这项工作有助于在观察性研究中更公平,更准确地代表患者群体.
- 未来的研究可以进一步完善该工具,并将其应用扩展到更广泛的健康数据环境中.
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