A novel solution of using deep learning for early prediction cardiac arrest in Sepsis patient: enhanced bidirectional long short-term memory (LSTM)
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-021-11176-5.pdf
Reference40 articles.
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3. Acharya UR, Fujita H, Oh SL, Hagiwara Y, Tan JH, Adam M (2017) Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Inform Sci 415–416:190–198. https://doi.org/10.1016/j.ins.2017.06.027
4. Acharya UR et al (2018) Automated identification of shockable and non-shockable life-threatening ventricular arrhythmias using convolutional neural network. Future Gen Comput Syst 79:952–959. https://doi.org/10.1016/j.future.2017.08.039
5. Akrivos E, Papaioannou V, Maglaveras N, Chouvarda I (2017) Prediction of cardiac arrest in intensive care patients through machine learning. In: Precision medicine powered by pHealth and connected health. Springer Singapore, pp 25–29
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