Every day, more than 115 people in the United States die after overdosing on opioids, making the opioid crisis the top current public health problem in the United States. The problem continues to worsen, as opioid overdoses increased 30 percent from July 2016 through September 2017 in 52 areas in 45 states. Effective methodologies and practical solutions are urgently needed in United States to prevent the worsening opioid crisis and combat opioid addiction and overdose.
On October 14th-15th, 2018, the University of California Institute for Prediction Technology (UCIPT) held a first ever opioid-focused two-day hackathon, aiming to bring together researchers and practitioners from multiple disciplines to rapidly design and develop implementable solutions across 4 opioid crisis-related tracks (two were focused on software development, with one having an ethics/data-sharing emphasis and the other being focused personalized behavior change apps) to combat opioid addiction crisis.
The hackathon featured the largest opioid data set of its kind in US history, which included alternative data from opioid related datasets, such as cannabis. The hackathon provides prizes included $5,000 for each of the 4 tracks, mentorship with design experts, and travel expenses for teams to continue meeting with key stakeholders throughout the country to further develop and implement their solutions into public health settings. Final winner selection is conducted with two rounds of strict judges: first round judges include Latecia Engram, MSPH, Lloyd Green (IEEE SA), Andrew Deming (Socrata), Dr. Shahram Lotfipour, MD (ICTS), Arielle Radin, Stephanie Soliz, and Claire Houlihan, and final round judges include Latecia Engram, MSPH, John Ives, PhD, April Rovero, Bharath Chakravarthy, MD, Chen Li, Ph.D., Lloyd Green (IEEE SA), and Sean Young, Ph.D.. Teams of computer and data scientists, public health officials and researchers from universities including UCLA, UCI, Stanford, Yale, CityU, etc, as well as industries (e.g., Oracle) and startups, traveled across the world to compete on the two-day hackathon and develop software and big data-based solutions to the opioid addiction crisis at Beckman Center, Irvine, CA, USA.
In this hackathon event, Prof. Qingpeng ZHNAG and Ph.D. candidate Jiandong ZHOU from the school of data science, CityU, collaborated with Ms. Bianca Giusto (Senior M&A Consultant, Ernst & Young, LLC) and Dr. Michael Masterman-Smith (Chief Scientific Officer, CA Labs) won the CHAMPION of the hackathon. The team’s multi-disciplinary backgrounds make them well-equipped for proposing data driven analytical solutions to combat opioid addiction. The team used open-source Google search data and cannabis data provided by the hackathon to predict opioid overdose and help reduce opioid usage. Predictive modeling using real-time Google search data shows that an increase in search rate of certain google search terms can indicate a rise in opioid-related deaths. Data analytics are proposed to reveal and visualize the relationship between cannabis consumption and opioid deaths. The visualization work of predictive data and analytical results of negative correlation between cannabis purchased and opioids prescribed in both states of Washington and Colorado will enable authorities to deploy resources and intervene before it is too late. In what follows, the team will continue to collaboratively further explore efficient data science methodologies and implement practical solutions into public health settings.
From
https://www.theopioidhackathon.com
https://twitter.com/predictech/status/1060672219016126464
https://predictiontechnology.ucla.edu/events/the-opioid-hackathon-2018/