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2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) (2016)
Washington, DC, USA
June 27, 2016 to June 29, 2016
ISBN: 978-1-5090-0944-2
pp: 130-139
ABSTRACT
Rare diseases are hard to identify and diagnose. Our goal is to use self-reported behavioural data to distinguish people with rare diseases from people with more common chronic illnesses. To this effect, we adapt a state of the art machine learning algorithm to make this classification. We find that using this method, and an appropriate set of questions, we can accurately identify people with rare diseases.
INDEX TERMS
Diseases, Sociology, Statistics, Drugs, Medical diagnostic imaging, Internet
CITATION

H. MacLeod, S. Yang, K. Oakes, K. Connelly and S. Natarajan, "Identifying Rare Diseases from Behavioural Data: A Machine Learning Approach," 2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), Washington, DC, USA, 2016, pp. 130-139.
doi:10.1109/CHASE.2016.7
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