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A deep learning algorithm to prevent burnout risk in Family Caregivers of patients undergoing dialysis treatment

机译:一种深度学习算法,可防止接受透析治疗的家庭护理人员的倦怠风险

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Continuous management of dialysis patients exposes family caregivers to significant psychophysical risks. Having demonstrated the effectiveness of artificial intelligence in the care and assistance processes, it was hypothesized the implementation of a mobile app which would detect the distress of these Family Caregivers in order to activate support actions and adequate sustenance. In order to identify the Burnout risk factors of the Family Caregivers of dialysis patients, 31 items were selected, from the questionnaires recognized in the literature, and submitted to a sample of 713 subjects. The four components extracted through factor analysis identify critical aspects of the family caregiver's experience. The neural network implemented on these four dimensions shows that overall they have an excellent ability to predict the stress state of the subjects (82%). From this study emerged the basic structure of a psychometric instrument suitable for the assessment of the stress of the family caregivers of patients undergoing dialysis treatment. This reagent can be administered through a mobile app and, using a deep learning algorithm, can report in real time the discomfort of the family caregivers.
机译:透析患者的持续管理使家庭护理人员面临重大的心理生理风险。在证明了人工智能在护理和协助过程中的有效性之后,有人假设实施了移动应用程序,该应用程序将检测这些家庭看护人的苦恼,以便激活支持行动和适当的养护。为了确定透析患者家庭照顾者的倦怠风险因素,从文献中认可的问卷中选择了31项,并提交给713名受试者的样本。通过因素分析提取的四个组成部分确定了家庭看护人经验的关键方面。在这四个维度上实现的神经网络表明,总体而言,它们具有预测受试者压力状态的出色能力(82%)。从这项研究中得出了一种心理测量仪器的基本结构,该仪器适用于评估接受透析治疗的患者的家庭护理人员的压力。该试剂可以通过移动应用程序进行管理,并且可以使用深度学习算法实时报告家庭护理人员的不适。

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