A novel automated depression detection technique using text transcript. Issue 1 (27th July 2022)

• Record Type: Journal Article
• Authors: Yadav, Uma; Sharma, Ashish K.
• Is Part Of: International journal of imaging systems and technology. Volume 33:Issue 1(2023)
• Page Start: 108
• Page End: 122
• Publication Date: 2022-07-27
• Subjects: BGRU -- deep learning -- depression detection -- depression levels -- PHQ score -- text embeddings -- text transcripts; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22793
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: Depression is one of the most common mental illnesses, impacting billions of people worldwide. The lack of existing resources is impeding the country's economic prosperity. As a result, new approaches for detecting and treating mental diseases as well as reaching out to individuals are required so that people can overcome their daily challenges and become more productive. An automated depression detection system can greatly aid in clinical findings and early treatment of depression. Automatic detection, like in a clinical interview can be derived from various modalities that include video, audio, and text. Among these modalities, audio characteristics are the most commonly researched while text elements are seldom investigated. In the light of the above, this paper proposes a novel automated depression identification approach based on linguistic material gathered from patient interviews. The focus is to enhance both the accuracy and efficiency of detection. The proposed approach is made up of two parts: a Bidirectional Gated Recurrent Unit (BGRU) network for dealing with linguistic information and a fully coupled network that integrates the model outputs to measure the depressed state. The proposed approach is validated using Distress Analysis Interview Corpus‐Wizard‐of‐Oz interviews dataset. To evaluate the performance precision, recall, and F1 score are computed using the proposed approach and then the comparative analysis is done with the existing approaches. The proposed approach yielded an F1 score of 0.92, indicating the existence of depression as well as the projected severity level. It is realized from the generated results that the proposed approach has outperformed the previous ones. The proposed approach can not only automatically assess the severity of depression but also enhances both the accuracy and efficiency of detection. The proposed approach indicates the feasibility of BGRU over Long Short Term Memory by achieving exceptional results for recognition of depression.