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

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.