Amazing technological breakthrough possible @S-Logix pro@slogix.in

Office Address

  • #5, First Floor, 4th Street Dr. Subbarayan Nagar Kodambakkam, Chennai-600 024 Landmark : Samiyar Madam
  • pro@slogix.in
  • +91- 81240 01111

Social List

Recurrent convolutional neural network based multimodal disease risk prediction - 2019

Recurrent Convolutional Neural Network Based Multimodal Disease Risk Prediction

Research Area:  Machine Learning

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 patients 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.

Keywords:  

Author(s) Name:  Yixue Hao,Mohd Usama,Jun Yang,M. Shamim Hossain and Ahmed Ghoneim

Journal name:  Future Generation Computer Systems

Conferrence name:  

Publisher name:  ELSEVIER

DOI:  10.1016/j.future.2018.09.031

Volume Information:  Volume 92, March 2019, Pages 76-83