Risk Prediction with Electronic Health Records: A Deep Learning Approach

By Yu Cheng, Fei Wang, Ping Zhang, Jianying Hu. 2016

425 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Health Information Management, Molecular Biology

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Citations in the corpus

Interpretable Representation Learning for Healthcare via Capturing Disease Progression through Time (2018, methodology): “Many other convolutional neural networks [4, 21] and recurrent neural networks [9, 25] have been applied to modeling EHR data for different prediction tasks, but their focus is slightly outside of the scope of this paper.”
Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep Learning (2017, background): “, phenotype extraction and health risk prediction [3], prediction of the development of various diseases, including schizophrenia, cancers, diabetes, heart failure, etc.”
Deep learning for healthcare: review, opportunities and challenges (2018, methodology): “pulmonary disease from longitudinal EHRs CNN [56]”
Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review (2018, methodology): “…from multiple modalities (eg., cognitive assessments, vital signs, medical images), and support both binary classification (eg., onset of disease 12,13 ) and multi-class classification (eg., classification of stages of Parkinson’s disease 14 ) Besides disease-specific multimodal data, some…”
Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis (2017, neutral): “[43], [45] [38], [44], [54] [52], [53], [60], [61] [13], [14]”
Mining Electronic Health Records: A Survey (2017, background): “; Cheng et al. 2016; Collins et al. 2011; Simon et al. 2011a; Mani et al. 2014; Matheny et al. 2010; Pakhomov et al. 2011; Fung et al. 2008; Wells et al. 2008; Vinzamuri et al. 2014; Li et al. 2015; van der Heijden et al. 2014; Jackson et al. 2011; Paxton et al. 2013; Zhao et al. 2011; Maroco et…”
Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records (2017, background): “in different healthcare applications including but not limited to computational phenotyping [6], [7], risk prediction [8], [9],”
The Internet of Things: Impact and Implications for Health Care Delivery (2020, background): “…include predicting risk, future medical outcomes, and care decisions in diabetes and mental health [57] and predicting the progression of congestive heart failure [58,59], bone disease [60], Alzheimer disease [61], benign and malignant tumor classification [62,63], and cardiac arrhythmias [64].”
Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks (2017, methodology): “etworks (RNNs) can be used for modeling multivariate time series data in healthcare with missing values [6, 18]. Convolutional neural networks (CNNs) are used to predictunplannedreadmission[23]andrisk[7]withEHR.Stacked denoising autoencoders(SDAs) are employed to detectthe characteristic paŠerns of ”
AI in Health: State of the Art, Challenges, and Future Directions (2019, methodology): “Matrix factorization [31] or CNN type of approaches [32] were then developed to analyze such matrices.”
Patient outcome prediction via convolutional neural networks based on multi-granularity medical concept embedding (2017, methodology): “[15] applied convolutional neural network (CNN) for prediction of congestive heart failure and chronic obstructive pulmonary disease.”