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Big Data and Optimization of Treatment Strategies

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Event
  • Session
  • Monday, 21 September 2015
  • 13:21 - 13:21
  • Duration: 31 mins
  • Publication date: 02 Oct 2015
  • Location: NA, Balliol College, Oxford, United Kingdom
  • Part of event Machine Learning in Healthcare

About the session

Machine learning has revolutionised many fields. With ever-increasing quantities of data being collected routinely throughout the healthcare system, there is an urgent need to develop new technologies based on machine learning for turning these very large and complex datasets into interventions that improve patient outcomes.

Keywords:
  • Healthcare Technologies TPN
  • TPN
  • data
  • disease
  • healthcare
  • infectious disease
  • technologies

Channels

Communities

Communities

Health care

Health care

Speaker

  • Dr Shamim  Nemati

    Dr Shamim Nemati

    I obtained my Ph.D. degree in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology (MIT) in 2013. While at MIT, I was a member of the Laboratory for Computational Physiology and Clinical Inference under the direction of Professor George C. Verghese, and a research fellow at the Brigham and Women's Hospital and the Harvard Medical School, where I held a National Research Service Award (NRSA) under the supervision of Professor Atul Malhotra. I have worked and published on several areas of research, including advanced signal processing and machine learning techniques, computational neuroscience/brain machine interface, physiological control systems, predictive monitoring in intensive care unit, and nonlinear and nonstationary multidimensional time-series analysis in massive temporal biomedical databases; resulting in over 35 peer-reviewed publications. I am currently a James S. McDonnell postdoctoral fellow in complex systems at the Harvard School of Engineering and Applied Sciences, and a member of the Harvard Intelligent Probabilistic Systems group. My project is titled: “Complex Physiological Systems and Big Data: Identifying and Exploiting Switching Dynamics in Multivariate Time Series.”
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