文献类型：会议论文

英文题名：An Effective Patient Representation Learning for Time-series Prediction Tasks Based on EHRs

作者：Lei, Liqi[1];Zhou, Yangming[1];Zhai, Jie[1];Zhang, Le[1];Fang, Zhijia[1];He, Ping[2];Gao, Ju[3]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]Shanghai Hosp Dev Ctr, Shanghai 200041, Peoples R China;[3]Shanghai Shuguang Hosp, Shanghai 200021, Peoples R China

会议论文集：IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Human Genomics

会议日期：DEC 03-06, 2018

会议地点：Madrid, SPAIN

语种：英文

外文关键词：Deep learning; representation learning; recurrent neural network; electronic health records

摘要：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 inhospital 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.