Research Area:  Machine Learning

Abstract:

We investigate the impact of using emotional patterns identified by the clinical practitioners and computational linguists to enhance the prediction capabilities of a mental illness detection (in our case depression and post-traumatic stress disorder) model built using a deep neural network architecture. Over the years, deep learning methods have been successfully used in natural language processing tasks, including a few in the domain of mental illness and suicide ideation detection. We illustrate the effectiveness of using multi-task learning with a multi-channel convolutional neural network as the shared representation and use additional inputs identified by researchers as indicatives in detecting mental disorders to enhance the model predictability. Given the limited amount of unstructured data available for training, we managed to obtain a task-specific AUC higher than 0.90. In comparison to methods such as multi-class classification, we identified multi-task learning with multi-channel convolution neural network and multiple-inputs to be effective in detecting mental disorders.

Keywords:  

Author(s) Name:  Prasadith Kirinde Gamaarachchige, D. Inkpen

Publisher name:  Semantic Scholar

DOI:  10.18653/v1/D19-6208

Volume Information:  

Paper Link:   https://www.semanticscholar.org/paper/Multi-Task%2C-Multi-Channel%2C-Multi-Input-Learning-for-Gamaarachchige-Inkpen/f63e347e336b744852bbb7d74663300c2d94bcd7