健康研究中数字非结构化数据丰富的挑战和最佳实践:一个系统的叙事审查
Jana Sedlakova1,2,3, Paola Daniore1,2, Andrea Horn Wintsch1,4,5
1Digital Society Initiative, University of Zurich, Zurich, Switzerland.
PLOS digital health
|October 11, 2023
概括
这项研究解决了研究中使用非结构化健康数据的挑战. 它提供了解决方案和检查清单,以改善数字非结构化数据的丰富性,以获得更好的健康见解和患者护理.
科学领域:
- 医疗信息学 医疗信息学
- 医学研究 医学研究
- 数据科学数据科学数据科学
背景情况:
- 数字健康数据至关重要,但往往是无结构的,无法用于研究.
- 非结构化数据需要大量的预处理,阻碍其与结构化数据进行整合以增强知识.
- 现有的文献缺乏对数字非结构化数据丰富的挑战和解决方案的全面概述.
研究的目的:
- 系统地审查健康研究中数字非结构化数据丰富的挑战和解决方案.
- 确定心脏病学,神经学和心理健康领域普遍存在的挑战领域.
- 为规划和评估使用非结构化健康数据进行研究的可行性制定实用检查清单.
主要方法:
- 关于数字非结构化数据丰富的文献的系统叙事审查.
- 识别和分类七个普遍存在的挑战领域.
- 为研究规划制定与数据流保持一致的检查清单.
主要成果:
- 确定了数字非结构化数据丰富的七个关键挑战领域.
- 建议解决方案,以克服数据预处理和集成中的方法障碍.
- 创建了一个检查清单,以指导研究人员进行规划和可行性评估.
结论:
- 数字非结构化数据丰富面临着重大的挑战,这些挑战限制了其研究潜力.
- 需要一个系统的方法和标准化的报告来实现可重复性.
- 开发的检查清单可以帮助研究人员更有效地利用非结构化健康数据.
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