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使用机器学习挖掘心理健康诊断组从COVID-19大流行之前和期间的急诊室演示

Carly Hudson¹, Grace Branjerdporn^2,3, Ian Hughes³

¹Bond University Faculty of Health Sciences and Medicine, Gold Coast, Queensland, Australia. chudson@bond.edu.au.

Discover mental health

|November 6, 2023

概括

在COVID-19期间,紧急心理健康护理的呈现增加了,但相对于所有紧急访问的比例下降了. 机器学习识别了封锁期间的趋势,为未来的流行病准备提供了信息.

科学领域:

公共卫生公共卫生
心理健康服务研究心理健康服务研究
医疗信息学医疗信息学

背景情况:

COVID-19 疫情对全球心理健康产生了重大影响.
医院急诊室对于管理心理健康危机至关重要.
分析紧急部门 (ED) 数据有助于服务规划和流行病准备.

研究的目的:

在COVID-19大流行之前和期间,分析五个心理健康介绍给EDs的趋势.
了解疫情对紧急心理健康服务利用的影响.
在公共卫生限制期间识别心理健康陈述的模式.

主要方法:

使用了机器学习框架MEDREADS (采矿紧急部门记录,进化算法数据搜索).
评估了来自澳大利亚两家公共医院ED的690,514次演示的数据 (2019年4月-2022年2月).
确定了与自杀倾向,精神病,躁狂,饮食障碍和物质使用有关的表现.

主要成果:

与流行病前水平相比,EDs的整体心理健康陈述在流行病期间增加.
与ED总访问相比,心理健康陈述的比例在流行期间下降了.
在公共卫生封锁和限制期间,出现频率的持续下降被观察到.

结论:

在COVID-19大流行期间,应用了新的机器学习技术来分析ED心理健康演示.
这些发现有助于人们更好地了解疫情期间紧急心理健康服务的使用情况.
结果突出了进一步调查心理健康表现模式的机会.

关键词:

危机护理危机护理数据挖掘是一种数据挖掘.应急部门的紧急情况部门.医疗数据分析健康数据分析.机器学习是机器学习.

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