References:
1. Sun Q, Qiu H, Huang M, Yang Y. Lower mortality of COVID-19 by early
recognition and intervention: experience from Jiangsu Province.Ann Intensive Care. 2020;10(1):33.
2. Phua J, Weng L, Ling L, et al. Intensive care management of
coronavirus disease 2019 (COVID-19): challenges and recommendations.Lancet Respir Med. 2020.
3. Tomasev N, Glorot X, Rae JW, et al. A clinically applicable approach
to continuous prediction of future acute kidney injury. Nature.2019;572(7767):116-119.
4. Fernandes M, Mendes R, Vieira SM, et al. Predicting Intensive Care
Unit admission among patients presenting to the emergency department
using machine learning and natural language processing. PLoS One.2020;15(3):e0229331.
5. Thorsen-Meyer H-C, Nielsen AB, Nielsen AP, et al. Dynamic and
explainable machine learning prediction of mortality in patients in the
intensive care unit: a retrospective study of high-frequency data in
electronic patient records. The Lancet Digital Health.2020;2(4):e179-e191.
6. Jiang X, Coffee M, Bari A, et al. Towards an Artificial Intelligence
Framework for Data-Driven Prediction of Coronavirus Clinical Severity.Computers, Materials & Continua. 2020;62(3):537-551.
7. Yan L, Zhang H-T, Goncalves J, et al. An interpretable mortality
prediction model for COVID-19 patients. Nature Machine
Intelligence. 2020;2(5):283-288.
8. S.J. Raudys AKJ. Small Sample Size Effects in Statistical Pattern
Recognition: Recommendations for Practitioners. IEEE Transactions
on Pattern Analysis and Machine Intelligence. 1991;13:252-264.