Name
Abstract | ||
|---|---|---|
With the rapid growth of biomedical and healthcare data, machine learning methods are used in more and more work to predict disease risk. However, most works use single-mode data to predict disease risk and only few works use multimodal data to predict disease risk. Thus, a new multimodal data-based recurrent convolutional neural network (MD-RCNN) for disease risk prediction is proposed. This model not only can use patient’s structured data and text data, but also can extract structured and unstructured features in fine-grained. Furthermore, in order to obtain the highly non-linear relationships between structured data and unstructured data, we use deep belief network (DBN)to fuse the features. Finally, we experiment with the medical big data of a Chinese two grade hospital during 2013–2015. Experimental results show that the accuracy of MD-RCNN algorithm can reaches 96% and outperforms several state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.future.2018.09.031 | Future Generation Computer Systems |
Keywords | Field | DocType |
Convolution neural network,Deep learning,Healthcare,Multimodal fusion | Disease,Convolutional neural network,Computer science,Deep belief network,Unstructured data,Artificial intelligence,Fuse (electrical),Data model,Big data,Machine learning,Distributed computing | Journal |
Volume | ISSN | Citations |
92 | 0167-739X | 4 |
PageRank | References | Authors |
0.45 | 27 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yixue Hao | 1 | 583 | 27.68 |
| Mohd Usama | 2 | 17 | 3.22 |
| jun yang | 3 | 7 | 1.56 |
| Mohammod Shamim Hossain | 4 | 268 | 34.68 |
| Ahmed Ghoneim | 5 | 256 | 23.72 |