Ensemble learning for poor prognosis predictions: A case study on SARS-CoV-2
| Autor: | Tony Whitehouse, Amitava Banerjee, Jiaxing Sun, Georgios V. Gkoutos, James T. Teo, Huayu Zhang, Tonny Veenith, Andreas Karwath, Zina M. Ibrahim, Fang Gao-Smith, Victor Roth Cardoso, Daniel Bean, Kevin Dhaliwal, Xin Zhang, Honghan Wu, Ting Shi, Xiaodong Wu, Bruce Guthrie, Kun Wang, Richard Dobson, Kezhi Li |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Adult
Male China Decision support system AcademicSubjects/SCI01060 Coronavirus disease 2019 (COVID-19) Computer science Calibration (statistics) Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Health Informatics Research and Applications Machine learning computer.software_genre Risk Assessment 01 natural sciences 03 medical and health sciences 0302 clinical medicine Humans 030212 general & internal medicine 0101 mathematics Set (psychology) AcademicSubjects/MED00580 Aged Aged 80 and over Models Statistical Ensemble forecasting SARS-CoV-2 business.industry 010102 general mathematics COVID-19 Middle Aged Prognosis Ensemble learning United Kingdom 3. Good health Female Artificial intelligence AcademicSubjects/SCI01530 business computer Predictive modelling |
| Zdroj: | Journal of the American Medical Informatics Association Journal of the American Medical Informatics Association : JAMIA J Am Med Inform Assoc |
| ISSN: | 1527-974X |
| DOI: | 10.1093/jamia/ocaa295 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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