Mental illness detection in social media can be considered a complex task, mainly due to the complicated nature of mental disorders. In recent years, this research area has started to evolve with the continuous increase in popularity of social media platforms that became an integral part of people's life. This close relationship between social media platforms and their users has made these platforms to reflect the users' personal life on many levels. In such an environment, researchers are presented with a wealth of information regarding one's life. In addition to the level of complexity in identifying mental illnesses through social media platforms, adopting supervised machine learning approaches such as deep neural networks have not been widely accepted due to the difficulties in obtaining sufficient amounts of annotated training data. Due to these reasons, we try to identify the most effective deep neural network architecture among a few of selected architectures that were successfully used in natural language processing tasks. The chosen architectures are used to detect users with signs of mental illnesses (depression in our case) given limited unstructured text data extracted from the Twitter social media platform.

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Web 2.0 technologies such as wikis, podcasts/vodcasting, blogs and semantic portals could be quite effective tools in e-learning for health professionals. If effectively deployed, such tools can offer a way to enhance students', clinicians' and patients' learning experiences, and deepens levels of learners' engagement and collaboration within medical learning environments. However, Web 2.0 requires simplicity of use as well as integration with modern web technologies. This article presents a Web 2.0 telemedical portal, which provides a social community-learning paradigm from the desk of the physician, the student, the hospital administrator, or the insurer. The presented portal utilises RESTful web services and techniques like content syndication, mushups and Asynchronous JavaScript API and XML (AJAX). The designed portal is based on the Apache Cocoon RESTful framework for sharing Digital Imaging and Communications in Medicine (DICOM) medical case studies. Central to this article is the integration between Cocoon and AJAX. The proposed AJAX-Cocoon portal utilises a JSP portlet architecture, which manages the interaction dynamics and overcomes the shortcomings of the JSR 168 and WSRP 1.0 standards.

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