Exploiting social media to enhance clinical decision support

Social media are increasingly used to collect data and report on the personal health status, on health issues, symptoms and experiences with treatments. The health data that is, thus, becoming available represents a valuable source for continuous health monitoring and prevention, and clinical decision making. Detecting diseases at early stage can help to overcome and treat them accurately. Social media data can be used to understand, in near real time, human life dynamics worldwide. These massive quantities of data could support in a wide range of medical and healthcare applications, including among others clinical trials and decision support. A Clinical Decision Support System (CDS) can greatly help in identifying diseases and methods of treatment. In this paper we propose a CDS framework that can integrate heterogeneous health data from different sources, such as laboratory test results, basic information of patients, health records and social media data. Using the electronic health medical data so collected, innovative machine learning and deep learning approaches are employed to implement a set of services to recommend a list of diseases and thus assist physicians in diagnosing or treating their patients' health issues more efficiently.