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A Machine Learning Algorithm to Predict Hypoxic Respiratory Failure and risk of Acute Respiratory Distress Syndrome (ARDS) by Utilizing Features Derived from Electrocardiogram (ECG) and Routinely Clinical Data

Publication ,  Preprint
Marshall, CE; Narendrula, S; Wang, J; De Souza Vale, JG; Jeong, H; Krishnan, P; Yang, P; Esper, A; Kamaleswaran, R
2022

Duke Scholars

DOI

Publication Date

2022
 

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Marshall, C. E., Narendrula, S., Wang, J., De Souza Vale, J. G., Jeong, H., Krishnan, P., … Kamaleswaran, R. (2022). A Machine Learning Algorithm to Predict Hypoxic Respiratory Failure and risk of Acute Respiratory Distress Syndrome (ARDS) by Utilizing Features Derived from Electrocardiogram (ECG) and Routinely Clinical Data. medRxiv. https://doi.org/10.1101/2022.11.14.22282274
Marshall, Curtis Earl, Saideep Narendrula, Jeffrey Wang, Joao Gabriel De Souza Vale, Hayoung Jeong, Preethi Krishnan, Phillip Yang, Annette Esper, and Rishi Kamaleswaran. “A Machine Learning Algorithm to Predict Hypoxic Respiratory Failure and risk of Acute Respiratory Distress Syndrome (ARDS) by Utilizing Features Derived from Electrocardiogram (ECG) and Routinely Clinical Data.” MedRxiv, 2022. https://doi.org/10.1101/2022.11.14.22282274.

DOI

Publication Date

2022