Abstract—The prediction of patient’s future health information from the historical electronic health records (EHR) forms the core of the development of personalized healthcare research tasks. Patient EHR data consists of sequences of visits over time, where each visit contains multiple medical codes, including diagnosis, medication, and patient profile. Using historical data from the EHR, we can predict medical conditions and medication uses. Existing works model EHR data by using recurrent neural networks (RNNs). However, RNN-based approaches have certain limitations: the performance of RNNs drops when the length of sequences is large and they ignore some of the characteristics of the patients themselves. We propose an application of using bidirectional RNNs to remember all the information of both the past and future visits and add some patient’s characteristics as side information into this model. Experimental results on real world EHR datasets show that the proposed model can remarkably improve the prediction accuracy when compared with the diagnosis prediction approaches, and it can provide clinically meaningful interpretation.
Index Terms—Component, electronic health records, bidirectional recurrent neural networks, side information
The authors are with College of information Science &Technology, Hainan University, Haikou, China and State Key Laboratory of marine resource utilization in the South China Sea, Hainan University (e-mail: firstname.lastname@example.org, huangmx09.com, email@example.com, firstname.lastname@example.org).
Cite: Yangzi Mu, Mengxing Huang, Chunyang Ye, and Qingzhou Wu, "Diagnosis Prediction via Recurrent Neural Networks," International Journal of Machine Learning and Computing vol. 8, no. 2, pp. 117-120, 2018.