来自第一个在线医疗分析平台Livingstone的现实世界证据. 其描述性流行病学模块的验证
Benjamin R Heywood1,2, Christopher Ll Morgan1, Thomas R Berni1
1Human Data Sciences, Cardiff, United Kingdom.
PLOS digital health
|July 25, 2023
概括
这项研究验证了Livingstone,一个实时医疗分析平台,显示其自动化疾病发病率和流行率估计与已发表的研究密切匹配. 这使得从常规健康数据中快速,可靠的描述性流行病学成为可能.
科学领域:
- 流行病学 流行病学
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
背景情况:
- 准确的疾病发病率和流行率数据对于公共卫生至关重要.
- 创建流行病学估计的传统方法是耗时的.
- 需要实时,自动化的方法来分析健康数据.
研究的目的:
- 为了验证利文斯通医疗分析平台的描述性流行病学模块.
- 将利文斯通的自动发生率和流行率估计与同行评审研究进行比较.
- 评估近实时流行病学数据生成的可靠性.
主要方法:
- 一个系统的搜索确定了10个相关的同行评审研究.
- 利文斯通的自动估计与这些研究的数据进行了比较.
- 林的一致性相关系数 (CCC) 用于验证.
- 来自临床实践研究数据链 (CPRD) 的数据被用作利文斯通的来源.
主要成果:
- 最终年度发病率的一致性为0.96,所有年度发病率为0.93.
- 终年流行率的一致性为1.00,全年流行率为0.93.
- 利文斯通证明了与已发表的流行病学发现有相当或近乎完美的一致性.
结论:
- 利文斯通平台可以自动生成可靠的描述性流行病学从常规健康记录在近实时.
- 这代表了流行病学研究和公共卫生监测的重大进展.
- 利文斯通提供了一个新的机制,用于快速,标准化的流行病学分析现实世界的数据.
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