基于人工智能的抑郁症辅助诊断方法  

AI-based assisted diagnostic methods for depression

作　　者：赵健[1] 周莉芸 武孟青 王雪珠 孟宪佳[1] ZHAO Jian;ZHOU Liyun;WU Mengqing;WANG Xuezhu;MENG Xianjia(School of Information Science and Technology,Northwest University,Xi’an 710127,China)

机构地区：[1]西北大学信息科学与技术学院,陕西西安710127

出　　处：《西北大学学报（自然科学版）》2023年第3期325-335,共11页Journal of Northwest University（Natural Science Edition）

基　　金：国家自然科学基金(62276211);陕西省国际科技合作计划重点项目(2021KWZ-07)。

摘　　要：抑郁症是当前高发的心理和精神疾病之一,传统诊断方式存在依赖医生经验的主观局限性,人工智能应用到抑郁症的检测中可以进行特定的数据分析,从而辅助提高心理与精神疾病的诊断效率。知识驱动的第一代抑郁症检测只能解决完全信息和结构化环境下的确定性问题,抑郁症相关特征的选择会直接影响识别结果;数据驱动的第二代抑郁症检测需要大量数据推动,仅依靠黑盒模型下得出的结果不够可信;第三代抑郁症检测通过信息融合,结合不同来源的异构信息,确保处理信息的高质量,充分利用前两代抑郁症分析系统各自的优势,手工提取的特征融合深度特征可以更好地挖掘到抑郁特征信息,决策融合具有很强的容错性,融合后的模型能增加结果的可靠度,更加全面地对抑郁症数据进行分析。该文对现有研究成果进行总结与分析,指出了人工智能抑郁症诊断研究未来的发展方向。Depression is one of the most prevalent psychological and psychiatric disorders today.Traditional diagnostic approaches have subjective limitations that rely on doctors’experience;artificial intelligence applied to the detection of depression can perform specific data analysis,thus assisting doctors engaged in the diagnosis of psychological and psychiatric disorders to improve diagnostic efficiency.The first generation of knowledge-driven depression detection can only solve the problem of certainty in a fully informed and structured environment,where the choice of depression-related features directly affects the recognition results.The second generation of data-driven depression detection needs to be driven by a large amount of data,and the results obtained under black box models alone are not credible enough.The third generation of depression detection ensures the processing of information through information fusion,combining heterogeneous information from different sources high quality,combining the respective advantages of the first two generations of depression analysis systems,manually extracted features combined with depth features can better tap into depression feature information,decision fusion is highly fault tolerant,and the fused model can increase the reliability of the results and provide a more comprehensive analysis of depression data.This paper examines a large amount of relevant literature,summarises and analyses existing research results,and points out the future development direction of AI depression diagnosis research.

关 键 词：人工智能 抑郁症诊断 机器学习 深度学习 信息融合 

分 类 号：TP391.7[自动化与计算机技术—计算机应用技术]