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在埃塞俄比亚西南部的一个医疗保健网络集群中使用汇总数据开发了一种患者流动可视化和预测模型

Balew Ayalew Kassie¹, Asaye Birhanu Senay^2,3, Geletaw Sahle Tegenaw^1,4

¹Faculty of Computing, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia.

PLOS digital health

|November 8, 2023

概括

这项研究使用数字数据可视化和预测埃塞俄比亚医疗保健中的患者流动. 开发的模型增强了现有系统,改善了实时患者监测和数据驱动的决策,以获得更好的健康结果.

科学领域:

医疗信息学医疗信息学
在医疗保健中的数据科学.
公共卫生系统分析公共卫生系统分析

背景情况:

埃塞俄比亚现有的健康信息系统,包括电子社区健康信息系统和地区健康信息系统,缺乏实时可视化和数据驱动的分析能力.
这种缺陷阻碍了持续监测患者流动和基于证据的决策.
目前的系统难以提供为优化医疗保健提供所必需的互动洞察力.

研究的目的:

解决埃塞俄比亚当前卫生信息系统的局限性.
开发一种可视化和预测医疗保健网络集群中的患者流动的模型.
加强实时数据分析,支持医疗保健中的基于证据的决策.

主要方法:

利用来自西南埃塞俄比亚21个医疗机构的41个月 (2019-2022) 的综合患者流量数据集.
使用Python Sankey图表开发了患者流动可视化.
应用机器学习算法,包括随机森林和K-Nearest Neighbors (KNN),用于流量建模和预测,使用NearMiss,SMOTE和SMOTE-Tomek技术解决数据不平衡.

主要成果:

使用随机森林和KNN算法在门诊流量建模 (0.85) 和预测 (0.83) 中实现了高精度.
在医疗保健网络集群中成功可视化了患者流动模式.
证明了使用先进数据分析来提高对患者运动的理解的可行性.

结论:

为埃塞俄比亚开发了一个基础的患者流动可视化和预测模型.
这个模型是医疗保健端到端,实时,数据驱动的分析仪表板的先驱.
研究结果强调,随着数字卫生系统的扩展,越来越需要有效的数据可视化和预测工具来支持决策.

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