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DTSTART:20190129T190000Z
DTEND:20190129T203000Z
SUMMARY:Infusing Surgical Data Science with Domain Knowledge to Improve Patient Outcomes
DESCRIPTION:Abstract:



Globally, there is demand for improved outcomes in surgery. With the incorporation of advanced sensors into the operating room, data science provides an avenue to improve outcomes. The diversity in disease, patients, and surgical techniques requires robust methods that are grounded in domain knowledge. In this talk, I present formulations for incorporating domain knowledge into modern surgical data science approaches. I also discuss how these approaches may improve several facets of surgery: (1) real-time decision support in the operating room, (2) evaluation of surgical approaches/technologies, (3) performance assessment and training, (4) increased surgical efficiency. I conclude with future directions on the integration of sequential, spatial, and pathological context into models of surgery and how to improve data acquisition in the operating room.



Bio:



Matthew Holden is a postdoctoral fellow at the Malone Center for Engineering in Healthcare at Johns Hopkins University. He completed his PhD (2018) and MSc (2014) in Computing at Queen's University. He received his BSc (2012) in Applied Mathematics & Physics from Western University. His research interest is in surgical data science, with emphasis on computer-assisted assessment and training. He is also a lead contributor to several open-source image-guided interventions software packages. His work is recognized through several awards, including fellowships from the Link Foundation, Mitacs – Campus France, and NSERC.



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