主 题：Are You Depressed? A Deep Learning Model to Identify People with Depression Based on Their Online Postings
主讲人: 李文文 (在读博士，香港大学)
时 间：2020-01-02 12:00
According to the World Health Organization, one in twenty people in the world have suffered from depression and emotional distress in the previous twelve months. When depression and emotional distress are comorbid with other diseases, the associated health outcomes will be worsened. How to manage and provide appropriate treatment to people suffering from depression and emotional distress is, therefore, a highly pressing issue. However, many people with depression and emotional distress are not sufficiently recognized and treated and do not actively seek help. It is therefore highly desirable to devise a method to effectively and proactively identify these people. Following the design science approach, we propose DK-LSTM (which stands for Domain Knowledge-enhanced Long Short-Term Memory), a novel design based on deep learning to identify people with depression and emotional distress. We conduct two experiments, one on a set of discussion forum postings in English and another on a set of blogs in Chinese, and the results show that the proposed design outperforms other machine learning classifiers and standard LSTM models. The research has important academic contributions and practical implications.
Wenwen Li is currently a fifth-year Ph.D. student in Information Systems in the Faculty of Business and Economics at the University of Hong Kong, under the direction of Dr. Michael Chau. The core of her research is data analytics. Her research interests include business analytics and business intelligence, health analytics and mobile health, machine learning, deep learning, natural language processing.
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