Title: Medical examination data prediction with missing information imputation based on recurrent neural networks

Authors: Han-Gyu Kim; Gil-Jin Jang; Ho-Jin Choi; Myungeun Lim; Jaehun Choi

Addresses: School of Computing, KAIST, Daejeon, Korea ' School of Electronics Engineering, Kyungpook University, Daegu, Korea ' School of Computing, KAIST, Daejeon, Korea ' Bio-Medical IT Convergence Research Division, Electronics and Telecommunications Research Institute, Daejeon, Korea ' Bio-Medical IT Convergence Research Division, Electronics and Telecommunications Research Institute, Daejeon, Korea

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.

Keywords: medical examination data prediction; recurrent neural network; long short-term memory; gated recurrent unit; bidirectional LSTM.

DOI: 10.1504/IJDMB.2017.090986

International Journal of Data Mining and Bioinformatics, 2017 Vol.19 No.3, pp.202 - 220

Received: 01 Aug 2017
Accepted: 09 Nov 2017
Published online: 05 Apr 2018 *

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