在心力衰竭研究中对编码的医疗保健数据进行了机器学习支持的系统审查
Asgher Champsi1,2, Karin T Slater3,4, Simrat Gill1
1Department of Cardiovascular Sciences, University of Birmingham, Medical School, Vincent Drive, Birmingham B15 2TT, UK.
European heart journal. Digital health
|January 23, 2026
概括
大约五分之一的心力衰竭研究使用编码的医疗保健数据. 机器学习有助于评估,但数据使用的透明度需要改进,需要像CODE-EHR这样的质量标准.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 临床研究 临床研究
背景情况:
- 编码的医疗保健数据在临床研究中越来越多地被使用.
- 报告数据使用的透明度对于研究完整性至关重要.
- 心力衰竭研究是数据分析的一个重要领域.
研究的目的:
- 评估心力衰竭研究中报告编码医疗保健数据的透明度.
- 利用机器学习对数据报告实践进行大规模评估.
- 为了确定近期心力衰竭研究中编码医疗保健数据的使用率.
主要方法:
- 系统地搜索EMBASE和MEDLINE (2015-2020) 的心力衰竭研究.
- 从研究样本中手动提取数据,以描述编码的医疗保健数据的使用情况.
- 开发和应用自然语言处理 (NLP) 模型,以自动审查更大的研究集.
主要成果:
- 在4279项确定的研究中,21.2%报告使用编码的医疗保健数据.
- 手动审查显示,只有47.5%的研究清楚地描述了数据集的构建和链接.
- 在识别数据使用时,NLP模型实现了高精度 (AUC 0.97,F1 0.96).
结论:
- 五分之一的当代心力衰竭研究报告使用编码的医疗保健数据.
- 机器学习促进了数据报告的可扩展评估.
- 有限的透明度需要研究中的医疗保健数据的质量标准 (例如,CODE-EHR).
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