کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
11012474 1798847 2019 27 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Recurrent convolutional neural network based multimodal disease risk prediction
ترجمه فارسی عنوان
پیش بینی خطر ابتلا به بیماری های چندجملهای مبتنی بر شبکه های عصبی پیچیده مجازی
کلمات کلیدی
شبکه عصبی محکم، یادگیری عمیق، مراقبت های بهداشتی، تلفیق چندجملهای،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Future Generation Computer Systems - Volume 92, March 2019, Pages 76-83
نویسندگان
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