Everyone is faced with the prospect of death, but not knowing when the time comes. Now, scientists have uncovered a disturbing benefit of artificial intelligence.
The unsettling research, published in PLOS One, is the result of researchers training to look at a decade's worth of health data from just over 500,000 people in the UK between the and 40 of 69 and assess their chances for dying prematurely. During the 2006-201
"We mapped the resulting predictions to mortality data from the cohort, using the Office of National Statistics death records, the UK cancer registry and 'hospital episodes' statistics, the study's lead author, University of Nottingham assistant professor of Epidemiology and Data Science Stephen Weng, said in a statement.
SOMEONE ALIVE TODAY WILL LIVE TO BE 1,000 YEARS OLD, 'INCENDIARY' TECH HONCHO CLAIMS ["Wefoundmachine-learnedalgorithmsweresignificantlymoreaccurateinpredictingdeaththanthestandardpredictionmodelsdevelopedbyahumanexpert"
To aid in the effort to understand premature mortality, Weng and his team of researchers looked at two different types of AI. The first is known as 'deep-learning,' in which information-centric networks. The second is 'random forest,' which is the live science reports of multiple, tree-like models to consider possible outcomes. " AI models, compared them to the more commonly used 'Cox regression' (which is based on age and gender) and found the returns of the AI models were far superior.
The deep learning algorithm correctly predicted 76 percent of the subjects who died during the random forest algorithm clocked in at 64 percent.
The different algorithms used are different factors, such as body fat, blood pressure and food consumption (random forest), as well as job-related hazards, air pollution and alcohol intake (machine learning). The Cox model, which largely relied on inputs as an ethnicity and physical activity, has been proved to be inferior.
"We have taken a major step forward in this field by developing a unique and holistic approach to predicting a person's risk of premature death by machine-learning, "Dr. Weng added in the statement.
While it may.
While this may be the case, it may be used as an example be a bit unsettling to think that a computer knows when it is up, preventative healthcare is likely to grow in popularity, as more people want to know what they are changing about their lifestyle in an effort to live longer and better lives.  IS SKYNET A REALITY AS TRUMP SIGNS EXECUTIVE ORDER ON ARTIFICIAL INTELLIGENCE, TECH GIANTS WARN OF DANGER
"There is currently intense interest in the potential to use AI 'or' machine-learning 'to better predict health outcomes, "said University of Nottingham professor Joe Kai, who said he worked on the study, in a statement." In some situations we may find it helpful, in others it may not.
Weng echoed Kai's sentiments, adding that the work has been going on for years to aid humanity.
"Preventative healthcare is a growing priority in the fight against serious illnesses in the general population, "Weng added." Most applications focus on a single disease area but predicting death due
In addition to longer life spans, there are also economic benefits to preventative healthcare.The Surgeon General has released a white paper that states a 1 percent reduction in "weight, blood pressure, glucose, and cholesterol risk factors would save $ 83 to $ 103 annually in medical costs per person," while a 5 perc ent reduction in hypertension would save $ 25 billion over 5 years.
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"Research from the Milken Institute suggested that a reduction in avoidable risk factors could lead to a gain of more than $ 1 trillion annually in laboratory supply and efficiency by 2023, "the white paper added.