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Found 8 papers, 0 papers with code
Improving Recurrent Neural Network Responsiveness to Acute Clinical Events
This work presents input data perseveration as a method of training and deploying an RNN model to make its predictions more responsive to newly acquired information: input data is replicated during training and deployment.
Interpreting a Recurrent Neural Network's Predictions of ICU Mortality Risk
Deep learning has demonstrated success in many applications; however, their use in healthcare has been limited due to the lack of transparency into how they generate predictions.
The Impact of Extraneous Variables on the Performance of Recurrent Neural Network Models in Clinical Tasks
Electronic Medical Records (EMR) are a rich source of patient information, including measurements reflecting physiologic signs and administered therapies.
Predicting Individual Responses to Vasoactive Medications in Children with Septic Shock
Conclusion: This initial attempt in pediatric critical care to predict individual physiologic responses to vasoactive dose changes in children with septic shock demonstrated an RNN model showed some improvement over a linear model.
Predicting Individual Physiologically Acceptable States for Discharge from a Pediatric Intensive Care Unit
Design: The means of each patient's hr, sbp and dbp measurements between their medical and physical discharge from the ICU were computed as a proxy for their physiologically acceptable state space (PASS) for successful ICU discharge.
The Dependence of Machine Learning on Electronic Medical Record Quality
There is growing interest in applying machine learning methods to Electronic Medical Records (EMR).
Modeling Missing Data in Clinical Time Series with RNNs
For linear models, we show an alternative strategy to capture this signal.
Learning to Diagnose with LSTM Recurrent Neural Networks
We present the first study to empirically evaluate the ability of LSTMs to recognize patterns in multivariate time series of clinical measurements.
