An Effective Patient Representation Learning for Time-series Prediction Tasks Based on EHRs

Abstract

Electronic Health Records (EHRs) provide possibilities to improve patient care and facilitate clinical research. However, there are many challenges faced by the applications of EHRs, such as temporality, high dimensionality, sparseness, noise, random error, and systematic bias. In particular, temporal patient information is difficult to effectively use by traditional machine learning methods while the sequential information of EHRs is very useful. In this paper, we propose a general-purpose patient representation learning approach to summarize sequential EHRs. Specifically, a recurrent neural network based denoising autoencoder is employed to encode in hospital records of each patient into a low dimensional dense vector. Based on EHR data collected from Shanghai Shuguang Hospital, we experimentally evaluate our proposed method on both mortality prediction and comorbidity prediction tasks. Experimental studies show that our proposed method outperforms other reference methods based on raw EHRs data. We also apply the “Deep Feature” represented by our method to track similar patients with t-SNE, which also achieves interesting results.