Artificial intelligence used to predict STDs
Artificial intelligence used to predict STDs
By Laura Kelly - The Washington Times - Monday, April 9,
2018
Syphilis has made a comeback in the U.S., and health
researchers are using Google searches and tweets in an effort to predict the
next outbreak.
After decades of steady decline, infection rates for the
sexually transmitted disease started to rise after hitting a historic low in
2000 — from 2.2 cases per 100,000 people to 8.7 cases per 100,000 in 2015 and
2016, according to the Centers for Disease Control and Prevention.
Researchers at the University of California at Los
Angeles working with the CDC have developed an artificial intelligence program
that can identify with high accuracy where and when a syphilis outbreak is
likely to occur based on Google search terms and social media comments related
to risky sexual behavior.
Lead researcher Sean Young, executive director of the
University of California Institute for Prediction Technology, said the
technology will allow public health officials to respond immediately to crises
and eliminate a lag time of up to five years between an outbreak and its
detection.
“Oftentimes, once the CDC finds out about disease
outbreaks, there have been so many cases that have already been spread and
transmitted that it’s just become a disaster,” Mr. Young told The Washington
Times. “So if we can get ahead of the curve, that would really help.”
In creating the artificial intelligence program,
researchers relied on syphilis incidence reports that hospitals and medical
providers across the country have been required to submit to health officials
since the 1940s.
They fed the program data on syphilis cases reported from
2012 to 2014 and had it compare this data with search terms and tweets
referencing risky sexual activity during the same period. The computer program
“learned” to detect patterns in social media posting and search queries as
indicative of an STD outbreak, pinpointing vulnerable areas at the county and
state levels.
The Google search portion of the research was published
last week in the journal Epidemiology, and the Twitter portion is in the April
issue of Preventive Medicine.
To protect the privacy of study participants, the
researchers didn’t identify the specific keywords they mined on Google but
generally described search queries including “sex,” “STD help,” “sex without a
condom,” “do I have an STD,” “symptoms of STD’s” and “how to find sex right
now.”
In the Twitter study, the researchers identified more
than 8,500 tweets, looking for “colloquial terms for intercourse” that were
often crude mentions of genitals or sex.
“They will tell you that they are drunk driving, they’ll
tell you the types of drugs they’re using, they’ll tell you who they’re having
sex with,” Mr. Young said of Twitter users his team observed.
“People are very comfortable putting anything and
everything out there, and they don’t really question it. … It’s become a social
norm that ‘I can and do share,’ and that’s the way, especially younger people,
connect with each other,” he added.
Syphilis manifests in four stages: first with genital
sores, then with a skin rash, swollen lymph nodes or fever. A period of no
symptoms can last from a few weeks to several years. But if the infection is
untreated, it can cause severe problems in the heart, brain or other organs.
When caught early, syphilis is treated with penicillin.
The researchers decided to test that the artificial
intelligence had accurately identified a pattern between the syphilis reports
and the internet data. They selected a time frame for which they had data about
syphilis cases and of which the program was not aware. They then fed it 144
weeks’ worth of social media comments and found that the program had a 90
percent accuracy rate in identifying where and when syphilis cases occurred.
“It’s not just identifying some association,” Mr. Young
said. “[We asked], can it be used to predict the future? And found that it was
able to.”
Scouring user data to predict an adverse event has
science-fiction overtones, and Mr. Young said it’s hard to discount comparisons
to “Minority Report,” the 2002 action film in which people are arrested before
they can commit crimes.
“It’s definitely Big Brother-ish … but we’re living in a
time where everything is Big Brother-ish,” he said.
Mr. Young, who is also an associate professor of family
medicine at UCLA, said his team is aware of the delicate balance between
helping the public and invasion of privacy. Follow-up interviews with research
subjects showed that they largely approved of using their data for public
health purposes, he said.
“Generally… people are saying, ‘Companies are already
monitoring everything we do, so if researchers and public health officials can
apply these same methods — but to promote public health and social good — then
we support you,’” he said.
Copyright © 2018 The Washington Times, LLC.
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