Assessing the effects of data drift on the performance of machine learning models used in clinical sepsis prediction-Reference-Cited by-同舟云学术

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Assessing the effects of data drift on the performance of machine learning models used in clinical sepsis prediction

Published:2023-05 Issue: Volume:173 Page:104930
ISSN:1386-5056
Container-title:International Journal of Medical Informatics
language:en
Short-container-title:International Journal of Medical Informatics

Author:

Rahmani Keyvan,Thapa Rahul,Tsou Peiling,Casie Chetty Satish,Barnes Gina,Lam Carson,Foon Tso Chak

Funder

National Institute on Alcohol Abuse and Alcoholism

National Institutes of Health

Publisher

Elsevier BV

Subject

Health Informatics

Reference38 articles.

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2. R. Alaiz-Rodríguez, N. Japkowicz, Assessing the Impact of Changing Environments on Classifier Performance, in: S. Bergler (Ed.), Adv. Artif. Intell., Springer Berlin Heidelberg, Berlin, Heidelberg, 2008: pp. 13–24. /10.1007/978-3-540-68825-9_2.

3. D.A. Cieslak, N.V. Chawla, A framework for monitoring classifiers’ performance: when and why failure occurs?, Knowl. Inf. Syst. 18 (2009) 83–108. /10.1007/s10115-008-0139-1.

4. B. Gyawali, K. Ramakrishna, A.S. Dhamoon, Sepsis: The evolution in definition, pathophysiology, and management, SAGE Open Med. 7 (2019) 205031211983504. /10.1177/2050312119835043.

5. F. Gul, M.K. Arslantas, I. Cinel, A. Kumar, Changing Definitions of Sepsis, Turk. J. Anesth. Reanim. 45 (2017) 129–138. /10.5152/TJAR.2017.93753.

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