A Tag Mining framework for Disease Inference from Health related data
Abstract
Normally people use Google to search their queries and that search engine respond them with the answer but that answer is in scattered format. User not gets exact answer for his / her queries. So we are going to implement this paper, we first report a user study on the information needs of health seekers in terms of questions and then select those that ask for possible diseases of their manifested symptoms for further analytic. We next propose a learning scheme to finding the possible diseases given the questions of health seekers. The proposed scheme comprises of two key components. The first globally mines the discriminate medical signatures from raw features. The second deems the raw features and their signatures as input nodes in one layer and hidden nodes in the subsequent layer, respectively. Meanwhile, it learns the inter-relations between these two layers via pre-training with pseudo labeled data. Following that, the hidden nodes serve as raw features for the more abstract signature mining. With incremental and alternative repeating of these two components, our scheme builds a sparsely connected deep architecture with three hidden layers. Overall, it well fits specific tasks with fine-tuning. Extensive experiments on a realworld dataset labeled by online doctors show the significant performance gains of our scheme.
References
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