医疗信息系统与健康互联网的未来

Olga Kolesnichenko, Gennady Smorodin, A. Mazelis, A. Nikolaev, L. Mazelis, A. Martynov, V. Pulit, S. Balandin, Yuriy Kolesnichenko
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引用次数: 5

摘要

本文介绍了Dell EMC外部研究和学术联盟提供的“iHealthCare Optimization”研究结果。在Python中使用聚类分析实现医疗信息系统qMS记录的大数据分析。聚类分析软件是由Andrey Mazelis(符拉迪沃斯托克国立经济与服务大学)创建的。聚类分析有两个方向:系列治疗(每个患者的调查程序数)和系列时间(每个患者等待调查程序的时间)。发现了两种患者管理模型(A模型和B模型),可以更好地规划护理管理。模型方法提供了在模式aaS中实现医疗保健标准的新功能,使用大数据分析后的反馈。约80-90%的原发性高血压患者可在日间医院接受治疗,无需住院。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
iPatient in medical information systems and future of internet of health
The results of Study “iHealthCare Optimization”, provided by Dell EMC External Research and Academic Alliances, are presented. Big Data analytics of Medical information system qMS records was implemented using cluster analysis in Python. Software for cluster analysis was created by Andrey Mazelis (Vladivostok State University of Economics and Service). There are two directions of cluster analysis: Series treatment (number of investigation procedures for each patient) and Series time (waiting time for investigation procedures for each patient). Two models of patients management (Model A and Model B) were found, that can be used for better planning of care management. Models approach provides the new capability to implement Health Care Standard in mode aaS, using feedback after Big Data analytics. Around 80-90% of patients with Essential hypertension can get treatment in Day Hospital without hospitalization.
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