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results of metabolic syndrome of patients in three consecutive years. In addition, it
summarized the final result into an extreme personalized treatment plan to assess
the dangerous factors and main treatment plans of patients. This way, doctors may
reduce morbidity by 50 % in the next 10 years by prescribing statins and helping
patients to lose weight by five pounds, or suggesting patients to reduce the total
triglyceride in their bodies if the sugar content in their bodies is over 20 %.
The Mount Sinai Medical Center in the U.S. utilizes technologies of Ayasdi, a big
data company, to analyze all genetic sequences of Escherichia Coli, including over
one million DNA variants, to know why bacterial strains resist antibiotics. Ayasdi's
technology uses Topological data analysis, a brand-new mathematic research
method, to understand data characteristics. HealthVault of Microsoft is an excellent
application of medical big data launched in 2007. The goal is to manage individual
health information in individual and family medical equipment. Presently, health
information can be entered and uploaded with mobile smart devices and imported
into individual medical records by a third-party agency. In addition, it can be
integrated with a third-party application with the software development kit (SDK)
and open interface.
6.3.5
Collective Intelligence
With the rapid development of wireless communication and sensor technologies,
mobile phones and tablet computers have integrated more and more sensors, with
increasingly stronger computing and sensing capacities. As a result, crowd sensing
is coming to the center stage of mobile computing. In crowd sensing, a large
number of general users utilize mobile devices as basic sensing units to conduct
coordination with mobile networks for distribution of sensed tasks and collection
and utilization of sensed data. The goal is to complete large-scale and complex
social sensing tasks. In crowd sensing, participants who complete complex sensing
tasks do not need to have professional skills. Crowd sensing modes represented by
Crowdsourcing has been successfully applied to geotagged photograph, positioning
and navigation, urban road traffic sensing, market forecast, opinion mining, and
other labor-intensive applications.
Crowdsourcing, a new approach for problem solving, takes a large number
of general users as the foundation and distributes tasks in a free and voluntary
way. Crowdsourcing can be useful for labor-intensive applications, such as pic-
ture marking, language translation, and speech recognition. The main idea of
Crowdsourcing is to distribute tasks to general users and to complete tasks that
users could not individually complete or do not anticipate to complete. With no
need for intentionally deploying sensing modules and employing professionals,
Crowdsourcing can broaden the sensing scope of a sensing system to reach the city
scale and even larger scales.
As a matter of fact, Crowdsourcing has been applied by many companies
before the emergence of big data. For example, P & G, BMW, and Audi improve
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