电子健康数据用于测量吸烟状态的有效性:系统性审查和元分析
Md Ashiqul Haque1, Muditha Lakmali Bodawatte Gedara1, Nathan Nickel1
1Department of Community Health Sciences, University of Manitoba, Winnipeg, MB, Canada.
BMC medical informatics and decision making
|February 2, 2024
概括
这次系统性审查评估了电子健康数据中的吸烟状态确定算法. 机器学习模型和链接数据源改善了算法有效性,尽管灵敏度和预测值各不相同.
科学领域:
- 医疗信息学 医疗信息学
- 公共卫生研究 公共卫生研究
- 在医疗保健中的数据科学.
背景情况:
- 吸烟是许多慢性疾病的重要危险因素.
- 电子健康数据库,包括电子医疗记录 (EMR) 和行政数据,对于人口健康研究至关重要.
- 关于吸烟确定算法的现有证据综合通常优先考虑慢性疾病而不是风险因素.
研究的目的:
- 系统地审查和元分析吸烟状态确定算法.
- 描述这些算法的特征并评估这些算法的有效性.
- 识别影响算法性能的因素.
主要方法:
- 按照PRISMA指南进行系统审查和元分析.
- 在EMBASE,MEDLINE,Scopus和Web of Science (1990-2022) 搜索了算法验证研究.
- 使用 QUADAS-2 工具评估偏差风险,并使用元回归来分析异质性.
主要成果:
- 从57个选定的文章中确定了116个吸烟状态确定算法,主要使用EMR数据 (71.6%).
- 对积极预测值,灵敏度和特异性的聚合估计分别为0.843,0.672和0.918.
- 基于机器学习的算法和使用EMR数据的算法显示了显著更高的灵敏度.
结论:
- 有许多算法用于确定电子健康数据中的吸烟状态,采用各种方法和数据源.
- 算法的有效性,特别是灵敏度和积极的预测值,因数据来源和方法的影响而有很大差异.
- 使用多个链接数据源的机器学习算法显示了提高吸烟状态确认准确性的前景.
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