Indexing Biosignal for Integrated Health Social Networks
Authors: 
Yi Huang, Insu SongAuthors Info & Claims
Yi Huang, Insu SongAuthors Info & ClaimsPages 133 - 141
Abstract
Rising medical costs and aging populations are major concerns for most countries, including developed countries. Some studies are now mining Health Social Networks (HSNs) as a way of dealing with these concerns. HSN provides a scalable, cost-effective, and fast method for collecting a large amount of user-generated data. However, patients usually have difficulty finding relevant information from social networks. This study aims to develop an Internet of Things (IoT) approach to find keywords to describe medical conditions using patients' biosignals. This study uses the Convolutional Neural Network (CNN) to encode ECG signals into word embedding vectors. Word embedding is a vector projection of words' sentimental features from a context. Similar keywords can be extracted given a vector. Therefore, keywords can be used to search for information from HSN. The average number of keywords correctly predicted is 2 to 3 out of 5. This approach improves the efficiency and effectiveness of information searching in HSNs using biosignal. This study is the first time that index biosignal in HSN.
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November 2019
214 pages
ISBN:9781450372992
DOI:10.1145/3375923
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- East China Normal University
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Publication History
Published: 25 March 2020
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ICBBE '19
ICBBE '19: 2019 6th International Conference on Biomedical and Bioinformatics Engineering
November 13 - 15, 2019
Shanghai, China
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