MHA: a multimodal hierarchical attention model for depression detection in social media.

IF 4.7 3区医学 Q1 MEDICAL INFORMATICS

Health Information Science and Systems Pub Date : 2023-01-18 eCollection Date: 2023-12-01 DOI:10.1007/s13755-022-00197-5

Zepeng Li, Zhengyi An, Wenchuan Cheng, Jiawei Zhou, Fang Zheng, Bin Hu

引用次数: 4

Abstract

As a serious mental disease, depression causes great harm to the physical and mental health of individuals, and becomes an important cause of suicide. Therefore, it is necessary to accurately identify and treat depressed patients. Compared with traditional clinical diagnosis methods, a large amount of real and different types of data on social media provides new ideas for depression detection research. In this paper, we construct a depression detection data set based on Weibo, and propose a Multimodal Hierarchical Attention (MHA) model for social media depression detection. Multimodal data is fed into the model and the attention mechanism is applied within and between modalities at the same time. Experimental results show that the proposed model achieves the best classification performance. In addition, we propose a distribution normalization method, which can optimize the data distribution and improve the accuracy of depression detection.

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MHA:社交媒体抑郁检测的多模态分层注意模型。

抑郁症作为一种严重的精神疾病，对个体的身心健康造成极大危害，成为自杀的重要原因。因此，有必要准确识别和治疗抑郁症患者。与传统的临床诊断方法相比，社交媒体上大量真实且不同类型的数据为抑郁症检测研究提供了新的思路。在本文中，我们构建了一个基于微博的抑郁症检测数据集，并提出了一个用于社交媒体抑郁症检测的多模式层次注意力（MHA）模型。多模态数据被输入到模型中，同时在模态内部和模态之间应用注意力机制。实验结果表明，该模型取得了最佳的分类性能。此外，我们提出了一种分布归一化方法，该方法可以优化数据分布，提高抑郁症检测的准确性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Health Information Science and Systems MEDICAL INFORMATICS-

CiteScore

11.30

自引率

5.00%

发文量

期刊介绍： Health Information Science and Systems is a multidisciplinary journal that integrates artificial intelligence/computer science/information technology with health science and services, embracing information science research coupled with topics related to the modeling, design, development, integration and management of health information systems, smart health, artificial intelligence in medicine, and computer aided diagnosis, medical expert systems. The scope includes: i.) smart health, artificial Intelligence in medicine, computer aided diagnosis, medical image processing, medical expert systems ii.) medical big data, medical/health/biomedicine information resources such as patient medical records, devices and equipments, software and tools to capture, store, retrieve, process, analyze, optimize the use of information in the health domain, iii.) data management, data mining, and knowledge discovery, all of which play a key role in decision making, management of public health, examination of standards, privacy and security issues, iv.) development of new architectures and applications for health information systems.