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Peers Health and U-M begin research partnership using disability and workers’ comp healthcare data

By | General Interest, Happenings, News, Research

Peers Health and the University of Michigan are starting a two-year research project that will apply advanced learning technologies to a proprietary global database of millions of de-identified disability and workers’ compensation cases. The goals of the project include developing a prescriptive modeling framework to facilitate development of optimal return-to-work plans for injured or ill patients.

Public policy experts have begun to connect patients’ ability to perform their productive endeavors, such as their job, to their state of general health and well-being. The findings from this project, by helping define when someone objectively has returned to health, could inform decision-making in virtually every healthcare episode.

The principal investigators in the project, Dr. Brian Denton and Dr. Jenna Wiens, are both renowned experts in medical machine learning. Dr. Denton, a professor of Industrial and Operations Engineering and Urology, and Dr. Wiens, an assistant professor of Computer Science and Engineering, are both affiliated with the Michigan Institute of Data Science (MIDAS) at U-M.

Peers Health recently announced an expanded partnership with ODG, an MCG company and part of the Hearst Health Network, to aggressively acquire new data to enhance ODG functionality and to fuel this research. Jon Seymour, MD, CEO of Peers, said, “This is a new phase in medical publishing where raw data collection is the editorial function and cutting-edge machine learning is the technology factor. We turned to the University of Michigan due to its impressive data science programs spanning multiple departments, as well as the specific experience of Dr. Denton and Dr. Wiens in medical applications. We’re confident this initiative will attract many new data contributors along the way.”

“The collaboration with Peers Health is exciting because it provides data that can help build a model that will reduce the time — from both a safety and productivity perspective — for people to return to work following sickness or injury,” Denton said. “Streaming data in from existing patients will allow our model to adapt and improve over time.”

Wiens added: “These data contain a particularly interesting training label: days away from work. We hypothesize that this will be a strong signal for the type, timing, and effectiveness of the treatments and therapies.”

The U-M partnership with Peers was established by MIDAS and the university’s Business Engagement Center (BEC).

“This partnership illustrates the power of combining data from the healthcare industry with the data science expertise of U-M faculty,” said Dr. Alfred Hero, professor of Engineering and co-director of MIDAS.

“It is energizing for the BEC to be part of these innovative collaborative relationships that create real impact in the world,” added BEC Director Amy Klinke.

 

PyData May Meetup: Scalable, Distributed, and Reproducible Machine Learning

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Join us for a PyData Ann Arbor Meetup on Thursday, May 25th at 6 PM, hosted by TD Ameritrade and MIDAS.

The recent advances in machine learning and artificial intelligence are amazing!  Yet, in order to have real value within a company, data scientists must be able to get their models off of their laptops and deployed within a company’s data pipelines and infrastructure.  Those models must also scale to production size data. In this talk, we will implement a model locally in Python. We will then take that model and deploy both it’s training and inference in a scalable manner to a production cluster with Pachyderm, an open source framework for distributed pipelining and data versioning. We will also learn how to update the production model online, track changes in our model and data, and explore our results.

Daniel Whitenack (@dwhitena) is a Ph.D. trained data scientist working with Pachyderm (@pachydermIO). Daniel develops innovative, distributed data pipelines which include predictive models, data visualizations, statistical analyses, and more. He has spoken at conferences around the world (ODSC, Spark Summit, Datapalooza, DevFest Siberia, GopherCon, and more), teaches data science/engineering with Ardan Labs (@ardanlabs), maintains the Go kernel for Jupyter, and is actively helping to organize contributions to various open source data science projects.

PyData Ann Arbor is a group for amateurs, academics, and professionals currently exploring various data ecosystems. Specifically, we seek to engage with others around analysis, visualization, and management. We are primarily focused on how Python data tools can be used in innovative ways but also maintain a healthy interest in leveraging tools based in other languages such as R, Java/Scala, Rust, and Julia.

PyData Ann Arbor strives to be a welcoming and fully inclusive group and we observe the PyData Code of Conduct. PyData is organized by NumFOCUS.org, a 501(c)3 non-profit in the United States.

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