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- W2894178883 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." @default.
- W2894178883 created "2018-10-05" @default.
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- W2894178883 date "2019-03-01" @default.
- W2894178883 modified "2023-10-10" @default.
- W2894178883 title "Recurrent convolutional neural network based multimodal disease risk prediction" @default.
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- W2894178883 doi "https://doi.org/10.1016/j.future.2018.09.031" @default.
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