论文标题

使用机器学习和虚拟认知行为疗法在COVID-19大流行中检测行为障碍检测的框架

Framework for Behavioral Disorder Detection Using Machine Learning and Application of Virtual Cognitive Behavioral Therapy in COVID-19 Pandemic

论文作者

Niger, Tasnim, Rayhan, Hasanur, Islam, Rashidul, Noor, Kazi Asif Abdullah, Hasan, Kamrul

论文摘要

在这个现代世界中,人们变得越来越以自我为中心和不社交。另一方面,人们受到压力,在Covid-19-19大流行状况中变得更加焦虑,并表现出行为障碍的症状。为了衡量行为障碍的症状,通常精神科医生会使用特定问卷的长时间课程和投入。这个过程很耗时,有时无法检测正确的行为障碍。此外,有时会毫不犹豫地遵循此过程。我们创建了一个数字框架,可以检测行为障碍并开出虚拟认知行为疗法(VCBT)以恢复。通过使用此框架,人们可以输入所需的数据,这些数据高度负责三种行为障碍,即抑郁症,焦虑和互联网成瘾。我们已经应用了机器学习技术来检测样品的特定行为障碍。该系统从任何时候通过VCBT引导用户对用户进行基本理解和治疗,这可能是用户有意识并进行正确治疗的垫脚石。

In this modern world, people are becoming more self-centered and unsocial. On the other hand, people are stressed, becoming more anxious during COVID-19 pandemic situation and exhibits symptoms of behavioral disorder. To measure the symptoms of behavioral disorder, usually psychiatrist use long hour sessions and inputs from specific questionnaire. This process is time consuming and sometime is ineffective to detect the right behavioral disorder. Also, reserved people sometime hesitate to follow this process. We have created a digital framework which can detect behavioral disorder and prescribe virtual Cognitive Behavioral Therapy (vCBT) for recovery. By using this framework people can input required data that are highly responsible for the three behavioral disorders namely depression, anxiety and internet addiction. We have applied machine learning technique to detect specific behavioral disorder from samples. This system guides the user with basic understanding and treatment through vCBT from anywhere any time which would potentially be the steppingstone for the user to be conscious and pursue right treatment.

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