@article{Wu_H-2020_172677,
title = {Ensemble learning for poor prognosis predictions: a case study on SARS-CoV2},
author = {Wu, H. and Zhang, H. and Karwath, A. and Ibrahim, Z. and Shi, T. and Zhang, X. and Wang, K. and Sun, J. and Dhaliwal, K. and Bean, D. and Cardoso, V. and Li, K. and Teo, J. and Banerjee, A. and Gao-Smith, F. and Whitehouse, T. and Veenith, T. and Gkoutos, G. and Wu, X. and Dobson, R. and Guthrie, B.},
month = {nov},
year = {2020},
abstract = {OBJECTIVE: Risk prediction models are widely used to inform evidence-based clinical decision making. However, few models developed from single cohorts can perform consistently well at population level where diverse prognoses exist (such as the SARS-CoV2 pandemic). This study aims at tackling this challenge by synergising prediction models from the literature using ensemble learning.},
pages = {791-800},
volume = {28},
issue = {4},
journal = {JAMIA: A Scholarly Journal of Informatics in Health and Biomedicine},
publisher = {Oxford University Press},
url = {https://doi.org/10.1093/jamia/ocaa295},
}
