{"title":"Disease Trajectories from Healthcare Data: Methodologies, Key Results, and Future Perspectives.","authors":"Isabella Friis Jørgensen, Amalie Dahl Haue, Davide Placido, Jessica Xin Hjaltelin, Søren Brunak","doi":"10.1146/annurev-biodatasci-110123-041001","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":29775,"journal":{"name":"Annual Review of Biomedical Data Science","volume":null,"pages":null},"PeriodicalIF":7.0000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annual Review of Biomedical Data Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1146/annurev-biodatasci-110123-041001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0
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.
疾病轨迹被定义为连续的、方向性的疾病关联,在全人口电子医疗数据的可用性和充足的计算能力的推动下,疾病轨迹已成为一个热门研究领域。在此,我们以欧洲的研究为重点,概述了疾病轨迹研究,包括用于构建疾病轨迹的本体论和计算方法。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、患者分层以及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,这些挑战和机遇最终将受益于欧洲健康数据空间(European Health Data Space)等倡议,随着时间的推移,这些倡议将使分析来自数亿患者队列的数据成为可能。
期刊介绍:
The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.