Preliminary detection of mild depression could immensely help in effective treatment of the common mental health disorder. Due to the lack of proper awareness and the ample mix of stigmas and misconceptions present within the society, mental health status estimation has become a truly difficult task. Due to the immense variations in character level traits from person to person, traditional deep learning methods fail to generalize in a real world setting. In our study we aim to create a human allied AI workflow which could efficiently adapt to specific users and effectively perform in real world scenarios. We propose a Hybrid deep learning approach that combines the essence of one shot learning, classical supervised deep learning methods and human allied interactions for adaptation. In order to capture maximum information and make efficient diagnosis video, audio, and text modalities are utilized. Our Hybrid Fusion model achieved a high accuracy of 96.3% on the Dataset; and attained an AUC of 0.9682 which proves its robustness in discriminating classes in complex real-world scenarios making sure that no cases of mild depression are missed during diagnosis. The proposed method is deployed in a cloud-based smartphone application for robust testing. With user-specific adaptations and state of the art methodologies, we present a state-of-the-art model with user friendly experience.
翻译:由于缺乏适当的认识以及社会内部存在的大量污名和错误观念,心理健康状况估计已成为一项真正困难的任务。由于人与人之间在性水平特征上的巨大差异,传统的深层次学习方法无法在现实世界环境中一概而论。在我们的研究中,我们的目标是创建一个与人相关的AI工作流程,能够有效地适应特定用户,并在现实世界情景中有效发挥作用。我们建议一种混合的深层次学习方法,将一次射击学习、古典受监督的深层次学习方法和人类相关互动的精髓结合起来,以便适应。为了获取最大限度的信息,并高效地诊断视频、音频和文本模式,我们采用了这一方法。我们的混合组合模型在数据集上实现了96.3%的高度精确度;并取得了0.9682的AUC,它证明了它在复杂的现实世界情景中歧视各阶层的稳健性,从而确保在诊断过程中不会错过任何轻微抑郁案例。拟议的方法被运用在基于云基的智能手机应用中,用于进行稳健的测试。我们采用了用户专用的调整和艺术方法的状态模型。我们采用了一种友好的用户使用。