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The Impact of Extraneous Variables on the Performance of Recurrent Neural Network Models in Clinical Tasks.

, , , , , and . CoRR, (2019)

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Improving Recurrent Neural Network Responsiveness to Acute Clinical Events., , , and . IEEE Access, (2021)Predicting Individual Responses to Vasoactive Medications in Children with Septic Shock., , , , , , and . CoRR, (2019)Improving Recurrent Neural Network Responsiveness to Acute Clinical Events., , , and . CoRR, (2020)Interpreting a recurrent neural network's predictions of ICU mortality risk., , , and . J. Biomed. Informatics, (2021)The Impact of Extraneous Variables on the Performance of Recurrent Neural Network Models in Clinical Tasks., , , , , and . CoRR, (2019)DRTED: Deep learning Recommendation of Treatment from Electronic Data., , , , and . AMIA, AMIA, (2016)Dynamic Mortality Risk Predictions in Pediatric Critical Care Using Recurrent Neural Networks., , , , , , and . CoRR, (2017)Predicting Individual Physiologically Acceptable States for Discharge from a Pediatric Intensive Care Unit., , , , and . CoRR, (2017)A novel application of spectrograms with machine learning can detect patient ventilator dyssynchrony., , , , , , , , , and . Biomed. Signal Process. Control., 86 (Part C): 105251 (September 2023)The impact of extraneous features on the performance of recurrent neural network models in clinical tasks., , , , , and . J. Biomed. Informatics, (2020)