Extracting psychiatric stressors for suicide from social media using deep learning

• Published in: BMC medical informatics and decision making Vol. 18; no. Suppl 2; pp. 43 - 87
• Main Authors: Du, Jingcheng, Zhang, Yaoyun, Luo, Jianhong, Jia, Yuxi, Wei, Qiang, Tao, Cui, Xu, Hua
• Format: Journal Article
• Language: English
• Published: London BioMed Central 23.07.2018; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Annotations; Artificial intelligence; Artificial neural networks; Automation; Behavior; Classifiers; Computational biology; Datasets; Deep Learning; Diagnosis; Digital media; Electronic health records; Health Informatics; Humans; Identification; Informatics; Information sharing; Information Systems and Communication Service; Innovations; Internet; Intervention; Keywords; Learning algorithms; Machine learning; Management of Computing and Information Systems; Medicine; Medicine & Public Health; Mental health; Named entity recognition; Natural language processing; Neural networks; Neural Networks, Computer; Performance enhancement; Psychiatric stressors; Recognition; Recurrent neural networks; Risk factors; Sentiment analysis; Social discrimination learning; Social interactions; Social Media; Social networks; Stress (Psychology); Stress, Psychological; Suicide; Suicide Prevention; Suicides & suicide attempts; Support vector machines; Transfer learning; United States > US; Deep learning; Suicide; Psychiatric stressors; Social media; Mental health; Named entity recognition
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-018-0632-8

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.