Year: 2018 Source: BMC Medical Informatics and decision making. (2018). SIEC No: 20180521

Background: Suicide has been one of the leading causes of deaths in the United States. One major cause of
suicide is psychiatric stressors. The detection of psychiatric stressors in an at risk population will facilitate the early
prevention of suicidal behaviors and suicide. In recent years, the widespread popularity and real-time information
sharing flow of social media allow potential early intervention in a large-scale population. However, few automated
approaches have been proposed to extract psychiatric stressors from Twitter. The goal of this study was to
investigate techniques for recognizing suicide related psychiatric stressors from Twitter using deep learning based
methods and transfer learning strategy which leverages an existing annotation dataset from clinical text.
Methods: First, a dataset of suicide-related tweets was collected from Twitter streaming data with a multiple-step
pipeline including keyword-based retrieving, filtering and further refining using an automated binary classifier.
Specifically, a convolutional neural networks (CNN) based algorithm was used to build the binary classifier. Next,
psychiatric stressors were annotated in the suicide-related tweets. The stressor recognition problem is
conceptualized as a typical named entity recognition (NER) task and tackled using recurrent neural networks (RNN)
based methods. Moreover, to reduce the annotation cost and improve the performance, transfer learning strategy
was adopted by leveraging existing annotation from clinical text.
Results & conclusions: To our best knowledge, this is the first effort to extract psychiatric stressors from Twitter
data using deep learning based approaches. Comparison to traditional machine learning algorithms shows the
superiority of deep learning based approaches. CNN is leading the performance at identifying suicide-related tweets
with a precision of 78% and an F-1 measure of 83%, outperforming Support Vector Machine (SVM), Extra Trees (ET),
etc. RNN based psychiatric stressors recognition obtains the best F-1 measure of 53.25% by exact match and 67.
94% by inexact match, outperforming Conditional Random Fields (CRF). Moreover, transfer learning from clinical
notes for the Twitter corpus outperforms the training with Twitter corpus only with an F-1 measure of 54.9% by
exact match. The results indicate the advantages of deep learning based methods for the automated stressors
recognition from social media