scholarly journals Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer

2020 ◽  
Vol 34 (01) ◽  
pp. 606-613
Author(s):  
Edward Choi ◽  
Zhen Xu ◽  
Yujia Li ◽  
Michael Dusenberry ◽  
Gerardo Flores ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Structure Learning ◽  
Learning Algorithm ◽  
Synthetic Data ◽  
Representation Learning ◽  
General Purpose ◽  
Health Records ◽  
Structure Information ◽  
Electronic Health ◽  
Graphical Structure

Effective modeling of electronic health records (EHR) is rapidly becoming an important topic in both academia and industry. A recent study showed that using the graphical structure underlying EHR data (e.g. relationship between diagnoses and treatments) improves the performance of prediction tasks such as heart failure prediction. However, EHR data do not always contain complete structure information. Moreover, when it comes to claims data, structure information is completely unavailable to begin with. Under such circumstances, can we still do better than just treating EHR data as a flat-structured bag-of-features? In this paper, we study the possibility of jointly learning the hidden structure of EHR while performing supervised prediction tasks on EHR data. Specifically, we discuss that Transformer is a suitable basis model to learn the hidden EHR structure, and propose Graph Convolutional Transformer, which uses data statistics to guide the structure learning process. The proposed model consistently outperformed previous approaches empirically, on both synthetic data and publicly available EHR data, for various prediction tasks such as graph reconstruction and readmission prediction, indicating that it can serve as an effective general-purpose representation learning algorithm for EHR data.

scholarly journals Deep representation learning of electronic health records to unlock patient stratification at scale

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Isotta Landi ◽  
Benjamin S. Glicksberg ◽  
Hao-Chih Lee ◽  
Sarah Cherng ◽  
Giulia Landi ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Representation Learning ◽  
Patient Stratification ◽  
Health Records ◽  
Electronic Health

scholarly journals Synthesizing electronic health records using improved generative adversarial networks

2018 ◽  
Vol 26 (3) ◽  
pp. 228-241 ◽  
Author(s):  
Mrinal Kanti Baowaly ◽  
Chia-Ching Lin ◽  
Chao-Lin Liu ◽  
Kuan-Ta Chen
Keyword(s):  
Electronic Health Records ◽  
Binary Data ◽  
Synthetic Data ◽  
Generative Adversarial Networks ◽  
Research Database ◽  
Data Generation ◽  
Generative Adversarial Network ◽  
Health Records ◽  
Adversarial Network ◽  
Electronic Health

AbstractObjectiveThe aim of this study was to generate synthetic electronic health records (EHRs). The generated EHR data will be more realistic than those generated using the existing medical Generative Adversarial Network (medGAN) method.Materials and MethodsWe modified medGAN to obtain two synthetic data generation models—designated as medical Wasserstein GAN with gradient penalty (medWGAN) and medical boundary-seeking GAN (medBGAN)—and compared the results obtained using the three models. We used 2 databases: MIMIC-III and National Health Insurance Research Database (NHIRD), Taiwan. First, we trained the models and generated synthetic EHRs by using these three 3 models. We then analyzed and compared the models’ performance by using a few statistical methods (Kolmogorov–Smirnov test, dimension-wise probability for binary data, and dimension-wise average count for count data) and 2 machine learning tasks (association rule mining and prediction).ResultsWe conducted a comprehensive analysis and found our models were adequately efficient for generating synthetic EHR data. The proposed models outperformed medGAN in all cases, and among the 3 models, boundary-seeking GAN (medBGAN) performed the best.DiscussionTo generate realistic synthetic EHR data, the proposed models will be effective in the medical industry and related research from the viewpoint of providing better services. Moreover, they will eliminate barriers including limited access to EHR data and thus accelerate research on medical informatics.ConclusionThe proposed models can adequately learn the data distribution of real EHRs and efficiently generate realistic synthetic EHRs. The results show the superiority of our models over the existing model.

scholarly journals Representation learning for clinical time series prediction tasks in electronic health records

2019 ◽  
Vol 19 (S8) ◽  
Author(s):  
Tong Ruan ◽  
Liqi Lei ◽  
Yangming Zhou ◽  
Jie Zhai ◽  
Le Zhang ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Time Series Prediction ◽  
Improve Patient Care ◽  
Representation Learning ◽  
Mortality Prediction ◽  
Systematic Bias ◽  
Health Records ◽  
Prediction Task ◽  
Electronic Health ◽  
Sequential Information

Abstract Background 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 information is difficult to effectively use by traditional machine learning methods while the sequential information of EHRs is very useful. Method In this paper, we propose a general-purpose patient representation learning approach to summarize sequential EHRs. Specifically, a recurrent neural network based denoising autoencoder (RNN-DAE) is employed to encode inhospital records of each patient into a low dimensional dense vector. Results Based on EHR data collected from Shuguang Hospital affiliated to Shanghai University of Traditional Chinese Medicine, we experimentally evaluate our proposed RNN-DAE method on both mortality prediction task and comorbidity prediction task. Extensive experimental results show that our proposed RNN-DAE method outperforms existing methods. In addition, we apply the “Deep Feature” represented by our proposed RNN-DAE method to track similar patients with t-SNE, which also achieves some interesting observations. Conclusion We propose an effective unsupervised RNN-DAE method to summarize patient sequential information in EHR data. Our proposed RNN-DAE method is useful on both mortality prediction task and comorbidity prediction task.

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