公平的健康信息学:为可验证和可解释的数据分析提供健康信息学框架
Muhammad Hameed Siddiqi1, Muhammad Idris2, Madallah Alruwaili1
1College of Computer and Information Sciences, Jouf University, Sakaka 73211, Saudi Arabia.
Healthcare (Basel, Switzerland)
|June 28, 2023
概括
一个新的健康信息学框架使得来自不同来源的实时数据相互关联,增强公共卫生调查并支持快速治愈. 该系统确保数据可查找,可访问,可互操作和可重复使用 (FAIR),以改善基于证据的研究.
科学领域:
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
- 公共卫生 公共卫生
背景情况:
- 随着COVID-19的流行,卫生信息学在快速调查和治疗开发方面出现了关键缺口.
- 现有的系统缺乏实时数据采集,关联和分析的强大框架,特别是敏感的健康数据.
- 在医疗保健中,对可验证,可重复和FAIR (可查找,可访问,可互操作,可重复使用) 数据管理的需求至关重要.
研究的目的:
- 提出一个新的健康信息学框架,用于实时数据采集,相关性和分析.
- 应对数据保密的挑战,确保健康研究的可核查性和可重复性.
- 增强连接各种数据源的能力,以便进行全面的医疗学科分析.
主要方法:
- 开发了一个框架,集成来自可穿戴传感器,临床调查,健康记录,学术出版物和语义本体学的实时数据 (例如,医学科目标题).
- 实施数据相关机制,以链接不同的信息,例如将可穿戴数据映射到健康记录和临床术语与试验.
- 设计了流数据采集,处理和生命周期管理的架构,确保数据遵循FAIR原则,并具有强大的身份和访问机制.
主要成果:
- 展示了一个实际的用例,将学术出版物和医学科目标题本体学的数据与临床研究数据相关联.
- 该框架成功地链接和关联了各种数据流,从而实现了全面的分析.
- 该架构支持整个数据管理生命周期的动态更新和可追溯性.
结论:
- 拟议的健康信息学框架为医疗保健中的实时数据集成和分析提供了强大的解决方案.
- 该系统提高了快速调查,证据生成和干预计划的能力.
- 该框架遵守FAIR原则并关注数据可追溯性对于推进公共卫生和医学研究至关重要.
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