The unprecedented growth of Internet users has resulted in an abundance of unstructured information on social media including health forums, where patients request health-related information or opinions from other users. Previous studies have shown that online peer support has limited effectiveness without expert intervention. Therefore, a system capable of assessing the severity of health state from the patients' social media posts can help health professionals (HP) in prioritizing the user's post. In this study, we inspect the efficacy of different aspects of Natural Language Understanding (NLU) to identify the severity of the user's health state in relation to two perspectives(tasks) (a) Medical Condition (i.e., Recover, Exist, Deteriorate, Other) and (b) Medication (i.e., Effective, Ineffective, Serious Adverse Effect, Other) in online health communities. We propose a multiview learning framework that models both the textual content as well as contextual-information to assess the severity of the user's health state. Specifically, our model utilizes the NLU views such as sentiment, emotions, personality, and use of figurative language to extract the contextual information. The diverse NLU views demonstrate its effectiveness on both the tasks and as well as on the individual disease to assess a user's health.

中文翻译：

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根据社交媒体帖子评估健康状况的严重程度

互联网用户的空前增长导致包括健康论坛在内的社交媒体上出现了大量非结构化信息，患者可以在这些论坛上请求其他用户提供与健康相关的信息或意见。先前的研究表明，如果没有专家干预，在线同伴支持的效果有限。因此，能够从患者的社交媒体帖子中评估健康状况严重程度的系统可以帮助健康专业人员 (HP) 确定用户帖子的优先级。在这项研究中，我们检查了自然语言理解 (NLU) 的不同方面的功效，以确定与两个观点（任务）相关的用户健康状况的严重程度 (a) 医疗状况（即，恢复、存在、恶化、其他) 和 (b) 药物治疗（即有效、无效、严重不良反应，其他）在线健康社区。我们提出了一个多视图学习框架，该框架对文本内容和上下文信息进行建模，以评估用户健康状况的严重程度。具体来说，我们的模型利用 NLU 视图（如情感、情感、个性和比喻语言的使用）来提取上下文信息。多样化的 NLU 视图证明了它在任务和个体疾病方面的有效性，以评估用户的健康状况。