在例行采集数据的观察性研究中对人口健康状况的算法开发,验证和评估的指导 (DEVELOP-RCD)
Wen Wang1,2,3, Ying-Hui Jin4, Mei Liu5,6,7
1Institute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-Based Medicine and Cochrane China Center, West China Hospital, Sichuan University, Chengdu, 610041, China. wangwen@wchscu.cn.
Military Medical Research
|August 6, 2024
概括
研究人员制定了指导方针,以改进观察性研究中使用的健康状况算法,使用例行收集的医疗保健数据 (RCD). 这旨在提高算法准确性和研究结果的有效性.
科学领域:
- 医疗信息学 医疗信息学
- 观测研究方法 观测研究方法
- 算法开发 算法开发
背景情况:
- 观察性研究越来越多地使用例行收集的医疗保健数据 (RCD).
- 从RCD中识别健康状况的算法在开发和验证方面存在显著差异.
- 低于最佳的算法性能威胁到基于RCD的研究结果的有效性.
研究的目的:
- 系统地开发并提供指导,用于创建,验证和评估RCD的健康状况识别算法.
- 解决在RCD研究中对标准化和强大的方法的关键需求.
主要方法:
- 对算法开发和验证方法进行叙述和系统审查.
- 在毒症识别算法上进行了实证研究.
- 制定了一个标准化的工作流程和13个最佳实践建议.
- 通过专家审查和共识会议完成了最终的指导.
主要成果:
- 建立了一个标准化的四步工作流:评估,开发,验证和评估算法.
- 为算法开发和验证制定了13条详细的良好实践建议.
- 通过败血症鉴定研究证明了指南的应用.
结论:
- 开发的指南促进了RCD研究中准确和适当的算法使用.
- 实施本指南可以提高使用RCD的观察研究结果的可信性和可靠性.
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