Risk Prediction with Electronic Health Records: A Deep Learning Approach
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Summary
A deep learning approach for phenotyping from patient EHRs by building a four-layer convolutional neural network model for extracting phenotypes and perform prediction and the proposed model is validated on a real world EHR data warehouse under the scenario of predictive modeling of chronic diseases.
- Type
- article
- Published
- 2016-06-30
- Cited by
- 430
- References
- 39
- OpenAlex
- https://openalex.org/W2511950764
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:31457108
Keywords
Deep learning, Computer science, Artificial intelligence, Machine learning, Convolutional neural network
References
- ADADELTA: An Adaptive Learning Rate Method
- Towards Personalized Medicine: Leveraging Patient Similarity and Drug Similarity Analytics
- Self-taught object localization with deep networks
- Effective Use of Word Order for Text Categorization with Convolutional Neural Networks
- An Exploration of Parameter Redundancy in Deep Networks with Circulant Projections
- Marble: high-throughput phenotyping from electronic health records via sparse nonnegative tensor factorization
- Composite distance metric integration by leveraging multiple experts' inputs and its application in patient similarity assessment
- Early versus late fusion in semantic video analysis
- From micro to macro: data driven phenotyping by densification of longitudinal electronic medical records
- Mining electronic health records: towards better research applications and clinical care
- PRINCIPLES OF NEURODYNAMICS. PERCEPTRONS AND THE THEORY OF BRAIN MECHANISMS
- Frequence: interactive mining and visualization of temporal frequent event sequences
- Large-Scale Video Classification with Convolutional Neural Networks
- Robust late fusion with rank minimization
- An Automated Model to Identify Heart Failure Patients at Risk for 30-Day Readmission or Death Using Electronic Medical Record Data
- Mining diabetes complication and treatment patterns for clinical decision support
- Dropout: a simple way to prevent neural networks from overfitting
- Towards heterogeneous temporal clinical event pattern discovery: a convolutional approach
- Gradient-based learning applied to document recognition
- A Convolutional Neural Network for Modelling Sentences
Cited by
- EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning
- Distributed Learning from Multiple EHR Databases: Contextual Embedding Models for Medical Events
- A Predictive Model for Medical Events Based on Contextual Embedding of Temporal Sequences
- Deep State Space Models for Computational Phenotyping
- Exploiting Convolutional Neural Network for Risk Prediction with Medical Feature Embedding
- Measuring Patient Similarities via a Deep Architecture with Medical Concept Embedding
- Mining Electronic Health Records (EHRs)
- Predicting healthcare trajectories from medical records: A deep learning approach
- Deep learning for healthcare: review, opportunities and challenges
- Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis
- Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks
- Preserving differential privacy in convolutional deep belief networks
- A Regularized Deep Learning Approach for Clinical Risk Prediction of Acute Coronary Syndrome Using Electronic Health Records
- HCNN: Heterogeneous Convolutional Neural Networks for Comorbid Risk Prediction with Electronic Health Records
- Survivability prediction of colon cancer patients using neural networks
- Predicting Discharge Medications at Admission Time Based on Deep Learning
- Finding Algebraic Structure of Care in Time: A Deep Learning Approach
- Preliminary exploratory data analysis of simulated national clinical data research network for future use in annotation of a rare tumor biobanking initiative
- Deep Similarity-Based Batch Mode Active Learning with Exploration-Exploitation
- Towards use of electronic health records: cancer classification
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