An AI algorithm to help identify homeless youth at risk of substance
abuse

Date:
August 14, 2020
Source:
Penn State
Summary:
While many programs and initiatives have been implemented to
address the prevalence of substance abuse among homeless youth
in the United States, they don't always include data-driven
insights about environmental and psychological factors that could
contribute to an individual's likelihood of developing a substance
use disorder. Now, an artificial intelligence (AI) algorithm could
help predict susceptibility to substance use disorder among young
homeless individuals, and suggest personalized rehabilitation
programs for highly susceptible homeless youth.



FULL STORY ========================================================================== While many programs and initiatives have been implemented to address the prevalence of substance abuse among homeless youth in the United States,
they don't always include data-driven insights about environmental and psychological factors that could contribute to an individual's likelihood
of developing a substance use disorder.


==========================================================================
Now, an artificial intelligence (AI) algorithm developed by researchers
at the College of Information Sciences and Technology at Penn State
could help predict susceptibility to substance use disorder among young homeless individuals, and suggest personalized rehabilitation programs
for highly susceptible homeless youth.

"Proactive prevention of substance use disorder among homeless youth
is much more desirable than reactive mitigation strategies such as
medical treatments for the disorder and other related interventions,"
said Amulya Yadav, assistant professor of information sciences and
technology and principal investigator on the project. "Unfortunately,
most previous attempts at proactive prevention have been ad-hoc in
their implementation." "To assist policymakers in devising effective
programs and policies in a principled manner, it would be beneficial
to develop AI and machine learning solutions which can automatically
uncover a comprehensive set of factors associated with substance use
disorder among homeless youth," added Maryam Tabar, a doctoral student in informatics and lead author on the project paper that will be presented
at the Knowledge Discovery in Databases (KDD) conference in late August.

In that project, the research team built the model using a dataset
collected from approximately 1,400 homeless youth, ages 18 to 26, in
six U.S. states. The dataset was collected by the Research, Education
and Advocacy Co-Lab for Youth Stability and Thriving (REALYST), which
includes Anamika Barman-Adhikari, assistant professor of social work at
the University of Denver and co-author of the paper.

The researchers then identified environmental, psychological and
behavioral factors associated with substance use disorder among them
-- such as criminal history, victimization experiences and mental
health characteristics. They found that adverse childhood experiences
and physical street victimization were more strongly associated with
substance use disorder than other types of victimization (such as sexual victimization) among homeless youth.

Additionally, PTSD and depression were found to be more strongly
associated with substance use disorder than other mental health disorders
among this population, according to the researchers.



========================================================================== Next, the researchers divided their dataset into six smaller datasets
to analyze geographical differences. The team trained a separate model
to predict substance abuse disorder among homeless youth in each of
the six states - - which have varying environmental conditions, drug legalization policies and gang associations. The team observed several location-specific variations in the association level of some factors, according to Tabar.

"By looking at what the model has learned, we can effectively find out
factors which may play a correlational role with people suffering from substance abuse disorder," said Yadav. "And once we know these factors,
we are much more accurately able to predict whether somebody suffers from substance use." He added, "So if a policy planner or interventionist
were to develop programs that aim to reduce the prevalence of substance
abuse disorder, this could provide useful guidelines." Other authors
on the KDD paper include Dongwon Lee, associate professor, and Stephanie Winkler, doctoral student, both in the Penn State College of Information Sciences and Technology; and Heesoo Park of Sungkyunkwan University.

Yadav and Barman-Adhikari are collaborating on a similar project through
which they have developed a software agent that designs personalized rehabilitation programs for homeless youth suffering from opioid
addiction. Their simulation results show that the software agent --
called CORTA (Comprehensive Opioid Response Tool Driven by Artificial Intelligence) -- outperforms baselines by approximately 110% in minimizing
the number of homeless youth suffering from opioid addiction.



==========================================================================
"We wanted to understand what the causative issues are behind people
developing opiate addiction," said Yadav. "And then we wanted to
assign these homeless youth to the appropriate rehabilitation program."
Yadav explained that data collected by more than 1,400 homeless youth
in the U.S. was used to build AI models to predict the likelihood of
opioid addiction among this population. After examining issues that
could be the underlying cause of opioid addiction -- such as foster care history or exposure to street violence -- CORTA solves novel optimization formulations to assign personalized rehabilitation programs.

"For example, if a person developed an opioid addiction because they
were isolated or didn't have a social circle, then perhaps as part of
their rehabilitation program they should talk to a counselor," explained
Yadav. "On the other hand, if someone developed an addiction because
they were depressed because they couldn't find a job or pay their bills,
then a career counselor should be a part of the rehabilitation plan."
Yadav added, "If you just treat the condition medically, once they go back
into the real world, since the causative issue still remains, they're
likely to relapse." Yadav and Barman-Adhikari will present their paper
on CORTA, "Optimal and Non- Discriminative Rehabilitation Program Design
for Opioid Addiction Among Homeless Youth," at the International Joint Conference on Artificial Intelligence-Pacific Rim International Conference
on Artificial Intelligence (IJCAI-PRICAI), which was to be held in July
2020 but is being rescheduled due to the novel coronavirus pandemic.

Other collaborators on the CORTA project include Penn State doctoral
students Roopali Singh (statistics), Nikolas Siapoutis (statistics)
and Yu Liang (informatics).


========================================================================== Story Source: Materials provided by Penn_State. Original written by
Jessica Hallman. Note: Content may be edited for style and length.


==========================================================================


Link to news story: https://www.sciencedaily.com/releases/2020/08/200814131014.htm

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