Researchers
have developed a complex system model to evaluate the health of populations in
some U.S. 
Sifting
through large amounts of data to determine which variables to use for the
assessment of things like the health of a city's population is challenging. 
Researchers
often choose these variables based on their personal experience. They might
decide that adult obesity rates, mortality rates, and life expectancy are
important variables for calculating a generalized metric of the residents'
overall health. But are these the best variables to use? Are there other more
important ones to consider?
Matteo
Convertino of Hokkaido  University  in Japan 
and Joseph Servadio of the University 
of Minnesota  in the U.S. 
Using
this method, Convertino and Servadio mined a large quantity of health data in
the U.S. U.S. 
They
found that some cities, such as Detroit San Francisco Philadelphia California Denver , Minneapolis 
and Chicago , appeared to perform poorly compared
to other regions, contrary to national city 
Convertino
believes that methods like this, fed by large datasets and analysed via
automated stochastic computer models, could be used to optimize research and practice;
for example, for guiding optimal decisions about health. "These tools can
be used by any country, at any administrative level, to process data in
real-time and help personalize medical efforts," says Convertino. 
But
it is not just for health data. "The model can be applied to any complexsystem to determine their optimal information network, in fields from ecology
and biology to finance and technology. Untangling their complexities and
developing unbiased systemic indicators can help improve decision-making
processes," Convertino added.





 
 
 
 
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