This document summarizes a presentation given by the director of the Data Science Institute at the University of Virginia about using a data science approach to address the problem of veteran suicide. It describes how a student team analyzed data to identify factors correlated with higher suicide rates among veterans, such as lack of health care access, social isolation, and less restrictive firearm laws. It then outlines the Data Science Institute's approach which includes acquiring and integrating data, performing machine learning and analysis, data visualization, and addressing ethical issues. The goal is to use these techniques to increase outreach to veterans and recommend policies to limit risks of suicide.

1. Veteran Suicide
Exemplifying the DSI Mission
Philip E. Bourne PhD
Stephenson Chair of Data Science
Director Data Science Institute
Based on the work of:
Rahul Batra, Anjalie Chauhan, Soukarya Ghosh, Paranya Jareonvongrayab, James Yun
May 22, 2018
1

2. Understanding what our DSI is about is best
told with a simple story…
The case of the trauma surgeon…
May 22, 2018 2

3. About the UVA DSI
• Independent &
interdisciplinary
• Awards MSDS degrees
• Awards dual degrees
• PhD program underway
• Research faculty being hired
• More about the structure of
the program in the context of
veteran suicide
May 22, 2018 3

4. Organization: core data science verticals
4
Data Integration
& Engineering
Machine Learning
& Analytics
Visualization
& Dissemination
Data Acquisition Ethics, Law,
Policy,
Social Implications
May 22, 2018

5. The Process
• North Virginia Technology Council
Announces a Hackathon
• Teams compete internally –
undergrads led by Daniel
Mietchen and Pete Alonso
• Team selected and competes
against GMU, VT, VCU
• UVA wins!
May 22, 2018 5

6. Source: U.S. Department of Veterans Affairs, US Census Bureau,
data.gov
The Problem
May 22, 2018 6
Latest Estimate – 20 vets commit suicide every day
In 2014, the latest year available, more than 7,400 veterans
took their own lives, accounting for 18 percent of all suicides
in America

7. Age Ranges
Veteran
Demographic
s
Period Served
Veteran Population
Total
Male
Female
Source: 2012, 2014, 2015 Data acquired from U.S. Department of Veterans Affairs, US Census Bureau, Department of
Defense
Race Distribution
Race Distribution
May 22, 2018 7

8. Veteran and General
Suicide
Victim Demographics
Suicide Rate by Age
Key
General Population
Veteran Population
Suicide by
Race
General
Population
Veteran Population
Suicide Rate by
Gender
Key
General Population
Veteran Population
Total Male Female
Source: 2012, 2014 data acquired from U.S. Department of Veterans Affairs, data.gov, 2014 Centers for Disease Control and Preventi
reportsMay 22, 2018 8

9. Correlation: Health Care by
State
● ~2M Veterans lack health insurance
● 42% Unaware of VA benefits
● Complicated priority system (VA)
○ False PTSD diagnosis – est.
47,000 undiagnosed each year
Source: 2014 data acquired from Veterans Affairs and
census.govMay 22, 2018 9

10. Source: 2014 data acquired from Veterans Affairs, data.gov and census.gov
Correlation: Social Isolation by State
Key
Rural
Urban
May 22, 2018 10

11. Source: 2014 data acquired from Veterans Affairs, data.gov and census.gov
Correlation: Social
Isolation as Measured by
State
Key
Rural
Urban
Utah
Arizona
New Mexico
Nevada
May 22, 2018 11

12. Source: 2014 data acquired from Veterans Affairs, data.gov and census.gov
Firearm Regulation
State Total Gun
Laws
Ammunition
Regulations
Background
Checks
Buyer
Regulations
Dealer
Regulations
Gun
Trafficking
Arizona 11 0 0 0 0 0
Nevada 11 1 0 0 0 0
New
Mexico
10 0 0 0 0 0
Utah 11 0 0 0 0 2
All
States
26.5 0.72 2.46 2.4 2.7 0.76
May 22, 2018 12

13. Firearm Access
Veteran Suicide
Firearm
Regulation
Source: 2014 data acquired from U.S. Department of Veterans Affairs
May 22, 2018 13

14. Social Media: Mining
APIs and URLs
57.69
%
90.00
%
100.00
%
Unsuccessful
**Data collected using various Python data mining methods and respective Social Media APIs
May 22, 2018 14

15. Social Media:
Veteran
Identification
Real Time Incoming
Tweet Feed
Keyword Mismatch
Keyword Match (i.e.
#Veteran, Navy,
Army)
Unique User Bio
Search Method
Bio Search failure
(various reasons)
Heat Sector Analysis
Null Data Retrieval
Text Analysis and
Aggregation of
Frequent Words
Heat Map
Subjective Behavioral
Pattern Analysis
Heat Keywords
Overall Veteran User
Base Sentiment
Key
Program
Method
Failure
Result
May 22, 2018 15

16. May 22, 2018 16

17. May 22, 2018 17

18. Community:
Content
Creation and
Mental Health
Training
Utilize social media
data to increase
outreach
Have VA Resources
complement private
health care
Recommendation
s
Limit firearm
possession based
on mental health
status
May 22, 2018 18

19. Data Acquisition
• Center for Biosensing
• Open Data Lab
• Wikipedian in Residence
• Capstone projects – synthetic
data
May 22, 2018 19

20. Data Integration & Engineering
• Support for Open Data
• Open Data Lab
• FAIR principles
• Capstone projects
• MOU with the Center for Open
Science
• Collaboration with the library
May 22, 2018 20

21. Machine Learning and Analysis
• Hackathons
• Cluster hires
• Focus of MSDS program
• Capstone projects
May 22, 2018 21

22. Visualization and Dissemination
• Wikipedian in Residence
• Open Data Lab
• Special electives
• Collaboration with the library
and COS
• Capstone projects
May 22, 2018 22

23. ELPSI
• Core course in the MSDS
• Emphasized throughout
• Capstone projects
• Establishing a Center for Ethics
of Data Science and the Public
Good
May 22, 2018 23

24. Open Questions
• Translation of interesting actions to social change?
• Emphasis on data engineering vs data analytics?
• How to maximize interdisciplinary research?
• How much emphasis on open science?
• How best to provide continuity and hence reproducibility?
• How to manage growth?
• Best model for exchange of best practices with other DSI’s?
• How best to instill best data science practices in our own
institutions?
May 22, 2018 24

25. Thank You!
May 22, 2018 25