tracking patient disease through symptons via sparse deep learning

Computer Engineering

Automatic disease inference is of significance to overcome any issues between what online health seekers with strange side effects need and what occupied human doctor with one-sided aptitude can offer. However, accurately and efficiently inferring diseases is non-trivial, especially for community-based health services due to the vocabulary gap, incomplete information, correlated medical concepts, and limited high quality training samples. Here the sparse deep learning algorithm is used as the data mining technique. Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using model architectures, with complex structures. The proposed scheme uses question-answering, deep learning as inferring methods. Some attributes used are raw features, medical attributes etc. The proposed scheme is comprised 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. . This paper present idea of deep learning architecture which is used in the health care domain for the diagnosis of diseases.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.