A Survey on Disease Inference to Limit Medical Verbiage Discrepancy between Online Health Seeker and Provider
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2015, Vol 3, Issue 11
Abstract
Bridging the gap between what online well-being seekers with unusual signs requisite and what busy human doctors with biased proficiency can offer is of greater importance. Online health care forums have been assisting well-being condition monitoring, illness modelling and validation of medical treatment by medical text mining. Precisely and competently concluding the diseases is non-trivial, especially for community-based health services due to the lexis gap, inadequate information, interrelated medical concepts, and incomplete high quality training samples. In this survey, the information needs of health seekers in terms of with their manifested symptoms are studied. An extensive learning structure is used to infer the diseases given the queries of health seekers. This scheme has two key components which first globally mines the discriminant medical signatures from raw features. And then it estimates the raw features and their signatures as input nodes in one layer and hidden nodes in the subsequent layers. The inter-relations between these two layers are found. All-encompassing trials on a real-world dataset labelled by online doctors show the noteworthy performance gains.
Authors and Affiliations
R. Meena Gomathi, Dr. S. Miruna Joe Amali
Keywords
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