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Wednesday, May 23, 2018

(Update) Data Science and Machine Learning for Healthcare

Cognitive Systems Institute Talk  

24 May 2018: 10:30 AM, ET   (Access Instructions below)

Talk by: Farah Shamout, Oxford University

Title: “Data Science and Machine Learning for Healthcare   

Abstract:     This talk will highlight the opportunities of AI in healthcare and how current advancements in the field are improving patient outcomes. By focusing on the use of electronic health records (EHRs), I will provide practical data preparation tips and summarize important considerations to keep in mind when using EHRs. Next, I will compare machine learning methods to Early Warning Scores, or the traditional predictors of acutely ill patients that are currently being used in hospitals. Finally, the talk will conclude with the limitations of translating research into clinical settings. 

Bio: Farah Shamout is a PhD student in Engineering Science in the Computational Health Informatics Laboratory at the University of Oxford. Shamout gained her BSc in Computer Engineering at New York University Abu Dhabi, before coming to Oxford as a Rhodes Scholar in 2016. Her DPhil focuses on machine learning systems developed using the HAVEN project, and aims to produce a hospital-wide alerting system to assess patients continuously based on machine learning methods and large-scale data acquisition from the Oxford University Hospitals NHS Foundation Trust and Portsmouth NHS Foundation Trust. Her research touches on both Bayesian nonparametrics and deep learning methods. 
Slides and Recording will be placed here.

Join the meetings by pointing your web browser to:  https://zoom.us/j/7371462221 ; Callin: (415) 762-9988 or (646) 568-7788 Meeting id 7371462221 ; International Numbers: https://zoom.us/zoomconference.

Slides and Recording will be placed here: http://cognitive-science.info/community/weekly-update/

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Replays before Dec 2015:  Dial 877.471.6587 or 402.970.2667 and enter the call’s Replay ID when prompted for a program ID number.   The Replay ID is listed in the Recording column of each date.

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