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将症状数据纳入纵向疾病轨迹中,以进行更详细的患者分层。

Incorporating symptom data in longitudinal disease trajectories for more detailed patient stratification.

机构信息

Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, DK-2200, Copenhagen, Denmark; Department of Pulmonary and Infection Medicine, Nordsjællands Hospital, DK-3400, Hillerød, Denmark.

Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, DK-2200, Copenhagen, Denmark.

出版信息

Int J Med Inform. 2019 Sep;129:107-113. doi: 10.1016/j.ijmedinf.2019.06.003. Epub 2019 Jun 4.

Abstract

OBJECTIVE

Use symptoms to stratify temporal disease trajectories.

MATERIALS AND METHODS

We use data from the Danish National Patient Registry to stratify temporal disease pairs by the symptom distributions they associate to. The underlying data comprise of 6.6 million patients collectively assigned with 7.5 million symptoms from chapter XVIII in the WHO International Classification of Disease version 10 terminology.

RESULTS

We stratify 33 disease pairs into 67 temporal disease-symptom-disease trajectories from three main diagnoses (two diabetes subtypes and COPD), where the symptom significantly changes the risk of developing the subsequent diseases. We combine these trajectories into three temporal disease networks, one for each main diagnosis. We confirm apparent relations between diseases and symptoms and discovered that multiple symptoms decrease the risk for diabetes progression.

CONCLUSION

Symptoms can be used to stratify disease trajectories, and we suggest that this approach can be applied to temporal disease trajectories systematically using structured claims data. The method can be extended to also use text-mined symptoms from unstructured data in health records.

摘要

目的

利用症状对疾病轨迹进行分层。

材料和方法

我们使用丹麦国家患者登记处的数据,根据与症状相关的分布对疾病对进行分层。基础数据包括来自世界卫生组织国际疾病分类第 10 版第十八章的术语的 660 万患者和 750 万症状。

结果

我们将 33 对疾病分为 67 种疾病-症状-疾病轨迹,涉及三种主要诊断(两种糖尿病亚型和 COPD),其中症状显著改变了随后疾病的发病风险。我们将这些轨迹合并为三个疾病时间网络,每个网络对应一个主要诊断。我们证实了疾病和症状之间的明显关系,并发现多种症状降低了糖尿病进展的风险。

结论

症状可用于分层疾病轨迹,我们建议使用结构化索赔数据系统地应用这种方法。该方法可以扩展到使用健康记录中的非结构化数据中提取的文本挖掘症状。

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