AI for healthcare

• Main Author: Neo, Nicholas Shun Xian
• Other Authors: Erik Cambria
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2021
• Subjects: Engineering::Computer science and engineering
• Online Access: https://hdl.handle.net/10356/153181
• Institution: Nanyang Technological University

Mental health has been an increasingly challenging issue to tackle in this era due to the stressful environment we are living in. One such example of mental health illness is depression. Depression is a mood disorder described as feelings of sadness, loss or anger that interfere with one’s everyday activities. Depression has existed as a problem in this society for many years. With technological advancements, social media platforms serve as a place for depressed personnel to seek help, hoping to feel better in one way or another. However, their problems are often neglected by others on the internet. If not detected quickly and accurately, one’s depression may develop into more serious issues such as suicidal thoughts. Research has shown that nearly 300 million people in the world suffer from depression every year. Measures to assess depression include clinical judgement or structured interviews, but a more common method is the use of social media analysis. Social media helps to detect depression by analysing posts on social media platforms. This method is preferred as expressing one’s feelings online has become the new norm, and processing of social media data can take place quickly, so authorities are able to intervene at an earlier stage. This project thus aims to analyse depressive texts from social media such as Twitter and Reddit by building various deep learning models for the different main tasks, hoping that we can detect depression and the cause of depression at an earlier stage. These main tasks include Classification, Emotion Intensity Prediction and Emotion-Cause Pair Extraction.