Center for Complex Network Research, Department of Physics, Northeastern University, Boston, USA.
Massachusetts Veterans Epidemiology and Research Information Center (MAVERIC), VA Boston Healthcare System, Boston, USA.
Sci Rep. 2022 Jul 14;12(1):12018. doi: 10.1038/s41598-022-15764-9.
A better understanding of the sequential and temporal aspects in which diseases occur in patient's lives is essential for developing improved intervention strategies that reduce burden and increase the quality of health services. Here we present a network-based framework to study disease relationships using Electronic Health Records from > 9 million patients in the United States Veterans Health Administration (VHA) system. We create the Temporal Disease Network, which maps the sequential aspects of disease co-occurrence among patients and demonstrate that network properties reflect clinical aspects of the respective diseases. We use the Temporal Disease Network to identify disease groups that reflect patterns of disease co-occurrence and the flow of patients among diagnoses. Finally, we define a strategy for the identification of trajectories that lead from one disease to another. The framework presented here has the potential to offer new insights for disease treatment and prevention in large health care systems.
更好地了解疾病在患者生命中发生的顺序和时间方面对于制定改进的干预策略至关重要，这些策略可以减轻负担并提高医疗服务质量。在这里，我们提出了一个基于网络的框架，使用来自美国退伍军人健康管理局（VHA）系统的超过 900 万患者的电子健康记录来研究疾病关系。我们创建了时间疾病网络，该网络映射了患者中疾病共同发生的顺序方面，并证明网络属性反映了相应疾病的临床方面。我们使用时间疾病网络来识别反映疾病共同发生模式和患者在诊断之间流动的疾病组。最后，我们定义了一种从一种疾病到另一种疾病的轨迹识别策略。这里提出的框架有可能为大型医疗保健系统中的疾病治疗和预防提供新的见解。
