Skip to content

July 3, 2014

Researchers teach a computer to predict teen binge drinkers

by John_A
image-210312.jpg

Some very run-of-the-mill beer

Intervention during someone’s teenage years is frequently the key to preventing alcohol abuse in adult life. It’s good to know, then, that a group of scientists has found a way to predict that abuse at an early age using computer modeling. The approach teaches the compute how to spot a likely teen binge drinker by weighing 40-plus biological and social factors that include brain structure, any enabling genes, past events and personality traits. If a 13-year-old is already smoking because of an addictive personality or family influences, for instance, it’s more likely that this child will pick up a dangerous drinking habit a few years later.

The early technology produced some false positives, but it was good enough to predict the likelihood of binging with 70 percent accuracy. It could become more reliable if given time — lead researcher Robert Whelan tells The Verge that he’d like future modelling to account for peer pressure from social networks, which wasn’t a major concern when prepping the study years earlier. While it’s doubtful that computer predictions will ever be completely accurate, they might get close enough that concerned parents and schools would often know when to take action.

[Image credit: Spencer Platt via Getty Images]

Filed under: Science

Comments

Via: The Verge

Source: Nature

About these ads
Read more from News

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s

Note: HTML is allowed. Your email address will never be published.

Subscribe to comments

Follow

Get every new post delivered to your Inbox.

Join 227 other followers

%d bloggers like this: