Individuals with connective tissue disorders such as Marfan syndrome(MFS)are often more susceptible to aortic aneurysms or tears. Large-scale population screening for this rare disease will, therefore, be useful in identifying people who are at higher risk of developing aortic dissection. For a tear to develop into type A dissection, while others into type B dissection, one hypothesis would be that it is due to different flow patterns generated close to the tear location and across the aorta. Although an initial model built from imaging can give good insights into the problem, this does not take into account progressive hemodynamic variation over time and the impact of lifestyle and daily activities. By incorporating ambulatory BP profiles, it is possible to create simulation results as a longitudinal model spanning over a longer period of time for a better understanding of disease progression as summarized in Fig. 3.
B. Social Health One of the primary tasks of telemedicine involves connecting patients and doctors beyond the clinic. However, this communication has been expanded, with the involvement of social networks, to new levels of social interaction. This new feature has opened up new possibilities of patient-to-patient communication regarding health beyond the traditional doctor-to-patient paradigm. One-fourth of patients with chronic diseases, such as diabetes, cancer, and heart conditions, are now using social network to share experiences with other patients with similar conditions, thereby providing another potential source of big data [15]. In addition to biological information, geolocation and social apps provide an additional feature to understand the behaviors and social demographics of patients, while avoiding resource intensive and expensive studies of large statistical sampling. This advantage has already been exploited by several epidemiological studies in areas, such as influenza outbreaks [16], [17], collective dynamics of smoking [18], and the misuse of antibiotics [19]. Text messages and posts on online social networks are also a valuable source of health information, e.g., for the better management of mental health. Compared to traditional methods, such as surveys, fluctuations and regulation of emotions, thoughts and behaviors analyzed over social network platforms, such as Twitter, offer new opportunities for the real-time analysis of expressed mood and its context [20]. For example, when validating against known patterns of variation in mood, the 2.73×109 emotional tweets collected over a 12week period in a study reported by Larsen et al. [20] claimed to find some correlation between emotion tweets and global health estimates from the World Health Organization on anxiety and suicide rates. Social media and internet searches can also be combined with environmental data, such as air quality data, to predict the sudden increase of asthma-related emergency visits [21].