Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records
The rapid growth of Electronic Health Records (EHRs), as well as the accompanied opportunities in Data-Driven Healthcare (DDH), has been attracting widespread interests and attentions. Recent progress in the design and applications of deep learning methods has shown promising results and is forcing massive changes in healthcare academia and industry, but most of these methods rely on massive labeled data. In this work, we propose a general deep learning framework which is able to boost risk prediction performance with limited EHR data. Our model takes a modified generative adversarial network namely ehrGAN, which can provide plausible labeled EHR data by mimicking real patient records, to augment the training dataset in a semi-supervised learning manner. We use this generative model together with a convolutional neural network (CNN) based prediction model to improve the onset prediction performance. Experiments on two real healthcare datasets demonstrate that our proposed framework produces realistic data samples and achieves significant improvements on classification tasks with the generated data over several stat-of-the-art baselines.
Code (0)
등록된 구현이 없습니다.
Tasks
Generative Adversarial NetworkPredictionSimilar Papers 제목 키워드 기반
Generating Multi-label Discrete Patient Records using Generative Adversarial Networks
Access to electronic health record (EHR) data has motivated computational advances in medical research. However, various concerns, particularly over privacy, can limit access to and collaborative use of EHR data. Sharing…
AttributeGenerative Adversarial NetworkSynthesizing Mixed-type Electronic Health Records using Diffusion Models
Electronic Health Records (EHRs) contain sensitive patient information, which presents privacy concerns when sharing such data. Synthetic data generation is a promising solution to mitigate these risks, often relying on …
Synthetic Data GenerationVocal Bursts Type PredictionGenerative Adversarial Regression (GAR): Learning Conditional Risk Scenarios
We propose Generative Adversarial Regression (GAR), a framework for learning conditional risk scenarios through generators aligned with downstream risk objectives. GAR builds on a regression characterization of condition…
Towards understanding ECG rhythm classification using convolutional neural networks and attention mappings
Access to electronic health record (EHR) data has motivated computational advances in medical research. However, various concerns, particularly over privacy, can limit access to and collaborative use of EHR data. Sharing…
Arrhythmia DetectionAttributeECG ClassificationElectrocardiography (ECG)+3Adversarial Training for Disease Prediction from Electronic Health Records with Missing Data
Electronic health records (EHRs) have contributed to the computerization of patient records and can thus be used not only for efficient and systematic medical services, but also for research on biomedical data science. H…
Disease PredictionImputationMissing ValuesPrediction+1