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Improving Recurrent Neural Network Responsiveness to Acute Clinical Events
2021
IEEE Access
Predictive models in acute care settings must immediately recognize precipitous changes in a patient's status when presented with data reflecting such changes. Recurrent neural networks (RNN) have become popular for clinical decision support models but exhibit a delayed response to acute events. New information must propagate through the RNN's cell state before the total impact is reflected in the model's predictions. Input data perseveration is a method to train more responsive RNN-based
doi:10.1109/access.2021.3099996
fatcat:7miryacikvgnfmq6c4rf67ix2a