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来自医疗保健数据的疾病轨迹:方法,关键结果和未来前景
Isabella Friis Jørgensen1, Amalie Dahl Haue1, Davide Placido1
1Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark;
Annual review of biomedical data science
|August 23, 2024
概括
这项研究回顾了疾病轨迹,利用大型医疗保健数据集绘制了连续疾病联系的地图. 它强调了欧洲的研究,方法和应用,如患者分层和个性化预测.
科学领域:
- 计算流行病学和生物信息学.
- 健康数据科学和预测分析.
背景情况:
- 疾病轨迹,代表着连续的疾病关联,是一个不断增长的研究领域.
- 由大型电子健康记录 (EHR) 和先进的计算能力驱动.
- 专注于欧洲研究,本体学和轨迹构造的计算方法.
研究的目的:
- 提供疾病轨迹研究的概述,强调欧洲的贡献.
- 讨论构建疾病轨迹的方法和使用的本体学.
- 探索各种应用,包括风险识别,疾病进展,患者分层和个性化预测.
主要方法:
- 审查关于疾病轨迹研究的现有文献,重点关注欧洲研究.
- 分析用于构建疾病轨迹的本体学和计算方法.
- 检查用于疾病进展和患者分层的机器学习应用程序.
主要成果:
- 疾病轨迹提供了从全人口医疗保健数据中对顺序性疾病关联的见解.
- 已建立的计算方法和本体学有助于构建和分析这些轨迹.
- 应用范围从描述性风险识别到先进的个性化预测.
结论:
- 疾病轨迹研究是一个快速发展的领域,具有巨大的潜力.
- 未来的进步将受到欧洲健康数据空间等倡议的支持,这使得大量患者队伍的分析成为可能.
- 计算方法和数据集成的持续发展将增强个性化医学和公共卫生战略.
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