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Section 2 - Targeting Effects

Published online by Cambridge University Press:  03 December 2019

Pedro L. Gambús
Affiliation:
Hospital Clinic de Barcelona, Spain
Jan F. A. Hendrickx
Affiliation:
Aalst General Hospital, Belgium
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Personalized Anaesthesia
Targeting Physiological Systems for Optimal Effect
, pp. 103 - 290
Publisher: Cambridge University Press
Print publication year: 2020

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References

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Further Reading

Amato, F, Lopez, A, Peña-Mendez, EM, Vaňharan Hampl, A, Havel, S: Artificial neural networks in medical diagnosis. J.Appl.Biomed. 2013; 11: 4758.Google Scholar
Hinton, G: Deep learning—a technology with the potential to transform health care. JAMA. 2018; 320: 1101–2.Google Scholar
Deo, RC: Machine learning in medicine. Circulation. 2015; 132: 1920–30.Google Scholar
Chen, JH, Asch, SM: Machine learning and prediction in medicine – beyond the peak of inflated expectations. N.Engl.J.Med. 2017; 376: 2507–9.Google Scholar
Doi, K: Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput.Med.Imaging.Graph. 2007; 31: 198211.Google Scholar
Obermeyer, Z, Emanuel, EJ: Predicting the future – Big data, machine learning, and clinical medicine. N.Engl.J.Med. 2016; 375: 1216–19.Google Scholar
Beam, AL, Kohane, IS: Big data and machine learning in health care. JAMA. 2018; 319: 1317–18.Google Scholar
Shen, D, Wu, G, Suk, HI: Deep learning in medical image analysis. Annu.Rev.Biomed.Eng. 2017; 19: 221–48.Google Scholar
Stead, WW: Clinical implications and challenges of artificial intelligence and deep learning. JAMA. 2018; 320: 1107–8.Google Scholar
Zhang, Z, Beck, MW, Winkler, DA, et al: Opening the black box of neural networks: methods for interpreting neural network models in clinical applications. Ann.Transl.Med. 2018; 6: 216.Google Scholar

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