Data science for mental health crises
Join us for PyData Pittsburgh's November event
Want to learn how data science can be used for more than targeting advertisements or building chatbots? Join PyData Pittsburgh for the talk Data Science for Mental Health Crises by Pim Welle, Chief Data Scientist at the Allegheny County Department of Human Services! We’ll be gathering in person on the evening of Wednesday, November 20, at the Code & Supply Workspace.
Please RSVP on Meetup and see below for more information about Pim and his talk.
On a bittersweet note, this will be our final event hosted at the Code & Supply Workspace and Community Center, which will be shutting down in December. (Code & Supply’s online activities and other events will continue as usual.) We’ve always moved our events around to different locations, but the Code & Supply Workspace has been one of the staples in our rotation. We’d like to thank Colin Dean and the rest of the team at Code & Supply for this tremendous resource they’ve provided to the technology community in Pittsburgh over the years.
If you have ideas for other great venues where we can host future PyData Pittsburgh events, please get in touch!
About the talk
In Data Science for Mental Health Crises, you'll hear how cutting-edge data science techniques can be used in government settings. By making use of machine learning / predictive analytics, survival modeling, and non-experimental causal inference, the Allegheny County Department of Human Services (DHS) did a complete profile of its mental health system, including a deep dive into involuntary commitments (sometimes called 302s).
The results of the analysis were staggering. This work led to a report and upcoming academic publication, which found that (1) 302s are very common, affecting 350 per 100k individuals (roughly in line with prison sentencing), (2) folks have very poor outcomes post 302, with 20% of the population passing away in 5 years and (3) the 302 population is very expensive - we spend 25% of our Medicaid behavioral health funding on 2% of the population. Moreover, using machine learning, we can detect the individuals who are likely to have poor outcomes from the moment they step foot in an inpatient unit. Lastly, the upcoming academic work highlights non-experimental causal inference techniques to show whether the commitment itself is helping or hurting downstream outcomes.
The resulting analysis is a use case that demonstrates how statistics, machine learning, and causal inference can all come together to help understand how our public systems are working.
About the speaker
Pim Welle is the Chief Data Scientist at Allegheny County Department of Human Services. He earned a degree in engineering from Massachusetts Institute of Technology and a PhD in engineering and public policy from Carnegie Mellon University. He has spent his career developing statistical and machine learning systems for governmental institutions. His current work is developing novel tooling within human services to evaluate systems for efficacy and to target resources to those individuals most in need.
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