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Ensemble learning for poor prognosis predictions: A case study on SARS-CoV-2

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Honghan Wu, Huayu Zhang, Andreas Karwath, Zina Ibrahim, Ting Shi, Xin Zhang, Kun Wang, Jiaxing Sun, Kevin Dhaliwal, Daniel Bean, Victor Roth Cardoso, Kezhi Li, James T. Teo, Amitava Banerjee, Fang Gao-Smith, Tony Whitehouse, Tonny Veenith, Georgios V. Gkoutos, Xiaodong Wu, Richard Dobson & 1 more Bruce Guthrie

Original languageEnglish
Pages (from-to)791-800
Number of pages10
JournalJournal of the American Medical Informatics Association : JAMIA
Volume28
Issue number4
DOIs
Published18 Mar 2021

Bibliographical note

Publisher Copyright: © The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association. Copyright: This record is sourced from MEDLINE/PubMed, a database of the U.S. National Library of Medicine

King's Authors

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-CoV-2 [severe acute respiratory syndrome coronavirus 2] pandemic). This study aims at tackling this challenge by synergizing prediction models from the literature using ensemble learning. MATERIALS AND METHODS: In this study, we selected and reimplemented 7 prediction models for COVID-19 (coronavirus disease 2019) that were derived from diverse cohorts and used different implementation techniques. A novel ensemble learning framework was proposed to synergize them for realizing personalized predictions for individual patients. Four diverse international cohorts (2 from the United Kingdom and 2 from China; N = 5394) were used to validate all 8 models on discrimination, calibration, and clinical usefulness. RESULTS: Results showed that individual prediction models could perform well on some cohorts while poorly on others. Conversely, the ensemble model achieved the best performances consistently on all metrics quantifying discrimination, calibration, and clinical usefulness. Performance disparities were observed in cohorts from the 2 countries: all models achieved better performances on the China cohorts. DISCUSSION: When individual models were learned from complementary cohorts, the synergized model had the potential to achieve better performances than any individual model. Results indicate that blood parameters and physiological measurements might have better predictive powers when collected early, which remains to be confirmed by further studies. CONCLUSIONS: Combining a diverse set of individual prediction models, the ensemble method can synergize a robust and well-performing model by choosing the most competent ones for individual patients.

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