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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Zhaohui Liang Gang Zhang Huang, J.X. Hu, Q.V. |
| Copyright Year | 2014 |
| Description | Author affiliation: Dept. of Comput. Sci. & Technol., East China Normal Univ., Shanghai, China (Hu, Q.V.) || Sch. of Autom., Guangdong Univ. of Technol., Guangzhou, China (Gang Zhang) || Sch. of Inf. Technol., York Univ., Toronto, ON, Canada (Zhaohui Liang; Huang, J.X.) |
| Abstract | Computer aid technology is widely applied in decision-making and outcome assessment of healthcare delivery, in which modeling knowledge and expert experience is technically important. However, the conventional rule-based models are incapable of capturing the underlying knowledge because they are incapable of simulating the complexity of human brains and highly rely on feature representation of problem domains. Thus we attempt to apply a deep model to overcome this weakness. The deep model can simulate the thinking procedure of human and combine feature representation and learning in a unified model. A modified version of convolutional deep belief networks is used as an effective training method for large-scale data sets. Then it is tested by two instances: a dataset on hypertension retrieved from a HIS system, and a dataset on Chinese medical diagnosis and treatment prescription from a manual converted electronic medical record (EMR) database. The experimental results indicate that the proposed deep model is able to reveal previously unknown concepts and performs much better than the conventional shallow models. |
| Starting Page | 556 |
| Ending Page | 559 |
| File Size | 691809 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781479956692 |
| DOI | 10.1109/BIBM.2014.6999219 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-11-02 |
| Publisher Place | UK |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Support vector machines Hypertension Training deep learning syndrome classification restricted Boltzmann machine deep belief network Brain modeling Data models unsupervised feature learning Medical diagnostic imaging |
| Content Type | Text |
| Resource Type | Article |
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