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Veteran Suicide: Exemplifying the DSI Mission

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Data Science Meeting, Manchester, May 22, 2018

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Veteran Suicide: Exemplifying the DSI Mission

  1. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 13. Firearm Access Veteran Suicide Firearm Regulation Source: 2014 data acquired from U.S. Department of Veterans Affairs May 22, 2018 13
  14. 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. 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. 16. May 22, 2018 16
  17. 17. May 22, 2018 17
  18. 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. 19. Data Acquisition • Center for Biosensing • Open Data Lab • Wikipedian in Residence • Capstone projects – synthetic data May 22, 2018 19
  20. 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. 21. Machine Learning and Analysis • Hackathons • Cluster hires • Focus of MSDS program • Capstone projects May 22, 2018 21
  22. 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. 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. 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. 25. Thank You! May 22, 2018 25

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