Medical examination data prediction with missing information imputation based on recurrent neural networks Online publication date: Thu, 05-Apr-2018
by Han-Gyu Kim; Gil-Jin Jang; Ho-Jin Choi; Myungeun Lim; Jaehun Choi
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 19, No. 3, 2017
Abstract: In this work, the recurrent neural networks (RNNs) for medical examination data prediction with missing information are proposed. Simple recurrent network (SRN), long short-term memory (LSTM) and gated recurrent unit (GRU) are selected among many variations of RNNs for the missing information imputation while they are also used to predict the future medical examination data. Besides, the missing information imputation based on bidirectional LSTM is also proposed to consider past information as well as the future information in the imputation process, while the traditional RNNs can only consider the past information during the imputation. We implemented medical examination results prediction experiment using the examination database of Koreans. The experimental results showed that the proposed RNNs worked better than the baseline linear regression method. Besides, the bidirectional LSTM performed best for missing information imputation.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Mining and Bioinformatics (IJDMB):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook