4 research outputs found
Suicidal Ideation and Mental Disorder Detection with Attentive Relation Networks
Mental health is a critical issue in modern society, and mental disorders
could sometimes turn to suicidal ideation without effective treatment. Early
detection of mental disorders and suicidal ideation from social content
provides a potential way for effective social intervention. However,
classifying suicidal ideation and other mental disorders is challenging as they
share similar patterns in language usage and sentimental polarity. This paper
enhances text representation with lexicon-based sentiment scores and latent
topics and proposes using relation networks to detect suicidal ideation and
mental disorders with related risk indicators. The relation module is further
equipped with the attention mechanism to prioritize more critical relational
features. Through experiments on three real-world datasets, our model
outperforms most of its counterparts
Detecting suicidal ideation with data protection in online communities
Recent advances in Artificial Intelligence empower proactive social services that use virtual intelligent agents to automatically detect people’s suicidal ideation. Conventional machine learning methods require a large amount of individual data to be collected from users’ Internet activities, smart phones and wearable healthcare devices, to amass them in a central location. The centralized setting arises significant privacy and data misuse concerns, especially where vulnerable people are concerned. To address this problem, we propose a novel data-protecting solution to learn a model. Instead of asking users to share all their personal data, our solution is to train a local data-preserving model for each user which only shares their own model’s parameters with the server rather than their personal information. To optimize the model’s learning capability, we have developed a novel updating algorithm, called average difference descent, to aggregate parameters from different client models. An experimental study using real-world online social community datasets has been included to mimic the scenario of private communities for suicide discussion. The results of experiments demonstrate the effectiveness of our technology solution and paves the way for mental health service providers to apply this technology to real applications