After the Ebola pandemic in 2013-14 where more than 11,000 people died, an independent review panel deemed the response sluggish and ineffective. Out of that report, health researchers are looking to become more proactive, by using machine learning to identify potential hot spots and even predict future outbreaks. The Department of Defense (DOD) is among the organizations leading the research. This interesting news came to us from MeriTalk in their article, “DOD Employs Machine Learning to Fight the Next Pandemic.”
Pandemic threats such as Ebola, Zika, and avian flu are not only a danger to public health, but are also viewed as potential national security threats.
Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that machines should be able to learn and adapt through experience.
Using machine learning and natural language processing may result in being able to identify signs of the disease earlier in the process and react to it sooner, thus limiting its impact. The technology is also designed to use machine learning to analyze social media data for early signs of an outbreak.
Melody K. Smith
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