Buddhitha Prasadith, Inkpen Diana
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada.
Front Res Metr Anal. 2023 Apr 17;8:1152535. doi: 10.3389/frma.2023.1152535. eCollection 2023.
Mental disorders and suicide are considered global health problems faced by many countries worldwide. Even though advancements have been made to improve mental wellbeing through research, there is room for improvement. Using Artificial Intelligence to early detect individuals susceptible to mental illness and suicide ideation based on their social media postings is one way to start. This research investigates the effectiveness of using a shared representation to automatically extract features between the two different yet related tasks of mental illness and suicide ideation detection using data in parallel from social media platforms with different distributions. In addition to discovering the shared features between users with suicidal thoughts and users who self-declared a single mental disorder, we further investigate the impact of comorbidity on suicide ideation and use two datasets during inference to test the generalizability of the trained models and provide satisfactory evidence to validate the increased predictive accurateness of suicide risk when using data from users diagnosed with multiple mental disorders compared to a single mental disorder for the mental illness detection task. Our results also demonstrate different mental disorders' impact on suicidal risk and discover a noticeable impact when using data from users diagnosed with Post-Traumatic Stress Disorder. We use multi-task learning (MTL) with soft and hard parameter sharing to produce state-of-the-art results for detecting users with suicide ideation who require urgent attention. We further improve the predictability of the proposed model by demonstrating the effectiveness of cross-platform knowledge sharing and predefined auxiliary inputs.
精神障碍和自杀被视为全球许多国家面临的全球性健康问题。尽管通过研究在改善心理健康方面已经取得了进展，但仍有改进的空间。利用人工智能根据社交媒体帖子早期检测易患精神疾病和有自杀意念的个体是一种可行的方法。本研究调查了使用共享表示法的有效性，该方法利用来自不同分布的社交媒体平台的并行数据，在精神疾病和自杀意念检测这两个不同但相关的任务之间自动提取特征。除了发现有自杀想法的用户和自我宣称患有单一精神障碍的用户之间的共享特征外，我们还进一步研究了共病对自杀意念的影响，并在推理过程中使用两个数据集来测试训练模型的通用性，为使用被诊断患有多种精神障碍的用户数据（相比于用于精神疾病检测任务的单一精神障碍用户数据）时自杀风险预测准确性的提高提供令人满意的证据。我们的结果还证明了不同精神障碍对自杀风险的影响，并发现使用来自被诊断患有创伤后应激障碍的用户数据时会产生显著影响。我们使用具有软参数共享和硬参数共享的多任务学习（MTL）来产生最先进的结果，以检测需要紧急关注的有自杀意念的用户。我们通过展示跨平台知识共享和预定义辅助输入的有效性，进一步提高了所提出模型的可预测性。
