Suppr超能文献

从医疗保健数据中预测疾病轨迹:方法、主要结果及未来展望。

Disease Trajectories from Healthcare Data: Methodologies, Key Results, and Future Perspectives.

机构信息

Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; email:

出版信息

Annu Rev Biomed Data Sci. 2024 Aug;7(1):251-276. doi: 10.1146/annurev-biodatasci-110123-041001.

Abstract

Disease trajectories, defined as sequential, directional disease associations, have become an intense research field driven by the availability of electronic population-wide healthcare data and sufficient computational power. Here, we provide an overview of disease trajectory studies with a focus on European work, including ontologies used as well as computational methodologies for the construction of disease trajectories. We also discuss different applications of disease trajectories from descriptive risk identification to disease progression, patient stratification, and personalized predictions using machine learning. We describe challenges and opportunities in the area that eventually will benefit from initiatives such as the European Health Data Space, which, with time, will make it possible to analyze data from cohorts comprising hundreds of millions of patients.

摘要

疾病轨迹,定义为连续的、有方向的疾病关联,已成为一个热门的研究领域,这得益于电子人群医疗保健数据的可用性和足够的计算能力。在这里,我们提供了疾病轨迹研究的概述,重点介绍了欧洲的工作,包括用作构建疾病轨迹的本体和计算方法。我们还讨论了疾病轨迹的不同应用,从描述性风险识别到疾病进展、患者分层和使用机器学习进行个性化预测。我们描述了该领域的挑战和机遇,最终将受益于欧洲健康数据空间等举措,随着时间的推移,这将使分析包含数亿患者的队列数据成为可能。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验