基于疾病的住院聚类:医院网络的疾病网络方法

Nouf Albarakati, Z. Obradovic
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引用次数: 4

摘要

为了提高医疗保健计划的质量,医疗保健系统在考虑不同因素的同时,面临着识别相似医院集群的挑战。基于入院行为对医院进行聚类将有所帮助,而对患者的诊断对于了解入院变化至关重要。因此,在考虑住院疾病症状相似性的情况下,对住院分布行为相似的医院进行分组是我们研究的目的。这是通过医院网络的疾病网络模型来实现的,该模型用于将多种疾病的住院分布表示为不同的医院网络,这些网络对应于顶层疾病网络中的疾病节点。这个疾病网络是从人类症状疾病网络中提取出来的,它模拟了不同疾病特异性医院网络之间的相似性。我们假设针对特定疾病的医院网络具有不同的底层聚类结构,如果对应的疾病具有相似的症状,则具有相同的底层聚类结构。研究人员对加州301家医院4年来每月收治的160种疾病的1400多万份电子健康记录进行了实验。在聚类过程中考虑疾病之间的相似性时,160个具有相似症状的特定疾病医院网络在这些网络中表现出一致的行为。当不考虑疾病之间的相似性时,缺乏一致行为模式的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Disease-Based Clustering of Hospital Admission: Disease Network of Hospital Networks Approach
To improve the quality of healthcare planning, healthcare systems face challenges in identifying clusters of similar hospitals while considering varying factors. Clustering hospitals based on their admission behavior would be helpful whereas diagnosis of patients is vital in understanding variation in admission. Therefore, grouping hospitals that show similar behavior on their admission distribution while considering similarity among disease symptoms in admission is the objective of our study. This is achieved by a Disease Network of Hospital Networks model which is used to represent hospital admission distribution of multiple diseases as different hospital networks that correspond to disease nodes in a top-layer disease network. This disease network that was extracted from the Human Symptoms Disease Network models the similarity among different disease-specific hospital networks. We assume that disease-specific hospital networks have different underlying clustering structure while share the same underlying clustering structure if corresponding diseases share similar symptoms. Experiments were conducted on more than 14 million electronic health records of monthly admission of 160 diseases over 4 years at 301 hospitals in California. Results of clustering 160 disease-specific hospitals networks that share similar symptoms among corresponding diseases show consistent behavior among these networks when similarity among diseases is considered in clustering process. Patterns of consistent behavior were lacking in results when similarity among diseases is not considered.
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