未经验的临床研究人员在数据驱动的假设生成:二次数据分析与可视化 (VIADS) 和其他工具的比较
Xia Jing1, James J Cimino2, Vimla L Patel3
1Department of Public Health Sciences, Clemson University, Clemson, SC.
medRxiv : the preprint server for health sciences
|June 19, 2023
概括
临床研究人员使用视觉交互分析工具 (VIADS) 更快地产生假设,但质量较差. 需要进一步开发,以改善VIADS用于假设生成.
科学领域:
- 临床研究 临床研究
- 数据分析数据分析数据分析.
- 医疗信息学 医疗信息学
背景情况:
- 数据驱动的假设生成在临床研究中至关重要.
- 现有的工具可能无法在这个过程中最好地支持研究人员.
- 视觉交互分析工具 (VIADS) 是为数据过和总结而开发的.
结论:
- 目前,VIADS对于产生假设的实用性尚不确定.
- 虽然VIADS加速了这个过程,但它可能会损害生成的假设的质量.
- 未来的VIADS代应该专注于提高假设的有效性,意义和可行性.
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