Xu Shihao, Yang Zixu, Chakraborty Debsubhra, Victoria Chua Yi Han, Dauwels Justin, Thalmann Daniel, Thalmann Nadia Magnenat, Tan Bhing-Leet, Chee Keong Jimmy Lee
Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul;2019:225-228. doi: 10.1109/EMBC.2019.8857071.
Schizophrenia and depression are the two most common mental disorders associated with negative symptoms that contribute to poor functioning and quality of life for millions of patients globally. This study is part of a larger research project. The overall aim of the project is to develop an automated objective pipeline that aids clinical diagnosis and provides more insights into symptoms of mental illnesses. In our previous work, we have analyzed non-verbal cues and linguistic cues of individuals with schizophrenia. In this study, we extend our work to include participants with depression. Powered by natural language processing techniques, we extract verbal features, both dictionary-based and vector-based, from participants' interviews that were automatically transcribed. We also extracted conversational, phonatory, articulatory and prosodic features from the interviews to understand the conversational and acoustic characteristics of schizophrenia and depression. Combining these features, we applied ensemble learning with leave-one-out cross-validation to classify healthy controls, schizophrenic and depressive patients, achieving an accuracy of 69%-75% in paired classification. From those same features, we also predict the subjective Negative Symptoms Assessment 16 scores of patients with schizophrenia or depression, yielding an accuracy of 90.5% for NSA2 but lower accuracy for other NSA indices. Our analysis also revealed significant linguistic and non-verbal differences that are potentially symptomatic of schizophrenia and depression respectively.
精神分裂症和抑郁症是与阴性症状相关的两种最常见的精神障碍，这些阴性症状导致全球数百万患者的功能受损和生活质量下降。本研究是一个更大研究项目的一部分。该项目的总体目标是开发一个自动化的客观流程，以辅助临床诊断并提供对精神疾病症状的更多见解。在我们之前的工作中，我们分析了精神分裂症患者的非语言线索和语言线索。在本研究中，我们将工作扩展到包括抑郁症患者。借助自然语言处理技术，我们从自动转录的参与者访谈中提取基于词典和基于向量的语言特征。我们还从访谈中提取了对话、发声、发音和韵律特征，以了解精神分裂症和抑郁症的对话和声学特征。结合这些特征，我们应用留一法交叉验证的集成学习来对健康对照、精神分裂症患者和抑郁症患者进行分类，在配对分类中准确率达到69%-75%。从这些相同的特征中，我们还预测了精神分裂症或抑郁症患者的主观阴性症状评估16得分，NSA2的准确率为90.5%，但其他NSA指标的准确率较低。我们的分析还揭示了分别可能是精神分裂症和抑郁症症状的显著语言和非语言差异。
