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应用网络科学进行关系型慢性病监测。

Applied Network Science for Relational Chronic Disease Surveillance.

作者信息

Shin Eun Kyong, Shaban-Nejad Arash

机构信息

The University of Tennessee Health Science Center - Oak-Ridge National Laboratory (UTHSC-ORNL), Center for Biomedical Informatics, Department of Pediatrics, Memphis, TN 38103 USA.

出版信息

Stud Health Technol Inform. 2019 Jul 4;262:336-339. doi: 10.3233/SHTI190087.

Abstract

Chronic diseases and conditions are the leading cause of death and disability in the United States. The number of people living with two or more chronic conditions has increased in the last decades and is expected to continue to rise over the upcoming years. Yet, traditional chronic disease surveillance practices have been specialized for a specific symptom or a single health condition. To better understand the complication and complexity of multimorbidity in chronic diseases, this paper suggests the use of network science for multimorbidity network surveillance (MNS). We discuss why the relational perspective in surveillance is critical and how network science can help and be integrated into surveillance and public health practice.

摘要

慢性病在美国是导致死亡和残疾的主要原因。在过去几十年里,患有两种或更多慢性病的人数有所增加,预计在未来几年还会继续上升。然而,传统的慢性病监测做法一直是针对特定症状或单一健康状况的。为了更好地理解慢性病中多重疾病的并发症和复杂性,本文建议使用网络科学进行多重疾病网络监测(MNS)。我们讨论了为什么监测中的关系视角至关重要,以及网络科学如何能够提供帮助并融入监测和公共卫生实践。

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