Big Data Based Recommendation Approaches for Healthcare
Recommender systems have attained widespread acceptance and have attracted the increased attention by the masses for over a decade. Recommender systems alleviate the complexities of products and services selection tasks and are meant to overcome the issues of information overload. Just like the recommender systems’ prospects in e-commerce and several other business domains, recommender systems have also been developed to offer recommendations about healthcare services and products. Considering the high volumes and dimensionality of healthcare data, utilization of efficient techniques to manage the big data is inevitable.
In this talk, we describe the need and rationale for using the big data enabled techniques for healthcare data. As case studies, we will detail our work on developing recommendation systems for: (a) health insurance products recommendation, (b) health expert recommendation from social media, (c) identification of influential doctors from Twitter, and (d) disease risk assessment services. During the discussion on the cases studies, we will discuss the following issues that are particular to the recommender systems: (a) cold start, (b) long-tail problem, and (c) scalability.
Date and Time
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- Date: 31 May 2018
- Time: 06:00 PM to 09:00 PM
- All times are (GMT-05:00) Canada/Eastern
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- 35 St. George St.
- Toronto, Ontario
- Canada M5S 1A4
- Building: University of Toronto (Galbraith Building)
- Room Number: GB405
- Click here for Map
- Starts 01 March 2018 10:01 AM
- Ends 31 May 2018 05:00 PM
- All times are (GMT-05:00) Canada/Eastern
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Speakers
Samee U. Khan, Ph.D. of Department of Electrical and Computer Engineering, North Dakota State University
Biography:
Samee U. Khan received a BS degree in 1999 from Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Pakistan, and a PhD in 2007 from the University of Texas, Arlington, TX, USA. Currently, he is Associate Professor of Electrical and Computer Engineering at the North Dakota State University, Fargo, ND, USA. Prof. Khan’s research interests include optimization, robustness, and security of systems. His work has appeared in over 300 publications. He is on the editorial boards of leading journals, such as IEEE Access, IEEE Communications Surveys and Tutorials, and IEEE IT Pro. He is an ACM Distinguished Speaker, an IEEE Distinguished Lecturer, a Fellow of the Institution of Engineering and Technology (IET, formerly IEE), and a Fellow of the British Computer Society (BCS).