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An ensemble prediction model for COVID-19 mortality risk.
Li, Jie; Li, Xin; Hutchinson, John; Asad, Mohammad; Liu, Yinghui; Wang, Yadong; Wang, Edwin.
  • Li J; School of Computer Science and Technology, Harbin Institute of Technology, 92 Xidazhi Street, Nangang District, Harbin, Heilongjiang 150006, China.
  • Li X; School of Computer Science and Technology, Harbin Institute of Technology, 92 Xidazhi Street, Nangang District, Harbin, Heilongjiang 150006, China.
  • Hutchinson J; Department of Medical Genetics, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada.
  • Asad M; Department of Medical Genetics, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada.
  • Liu Y; School of Computer Science and Technology, Harbin Institute of Technology, 92 Xidazhi Street, Nangang District, Harbin, Heilongjiang 150006, China.
  • Wang Y; School of Computer Science and Technology, Harbin Institute of Technology, 92 Xidazhi Street, Nangang District, Harbin, Heilongjiang 150006, China.
  • Wang E; Department of Medical Genetics, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada.
Biol Methods Protoc ; 7(1): bpac029, 2022.
Article in English | MEDLINE | ID: covidwho-2316518
ABSTRACT

Background:

It's critical to identify COVID-19 patients with a higher death risk at early stage to give them better hospitalization or intensive care. However, thus far, none of the machine learning models has been shown to be successful in an independent cohort. We aim to develop a machine learning model which could accurately predict death risk of COVID-19 patients at an early stage in other independent cohorts.

Methods:

We used a cohort containing 4711 patients whose clinical features associated with patient physiological conditions or lab test data associated with inflammation, hepatorenal function, cardiovascular function, and so on to identify key features. To do so, we first developed a novel data preprocessing approach to clean up clinical features and then developed an ensemble machine learning method to identify key features.

Results:

Finally, we identified 14 key clinical features whose combination reached a good predictive performance of area under the receiver operating characteristic curve 0.907. Most importantly, we successfully validated these key features in a large independent cohort containing 15 790 patients.

Conclusions:

Our study shows that 14 key features are robust and useful in predicting the risk of death in patients confirmed SARS-CoV-2 infection at an early stage, and potentially useful in clinical settings to help in making clinical decisions.
Keywords

Full text: Available Collection: International databases Database: MEDLINE Type of study: Cohort study / Observational study / Prognostic study Language: English Journal: Biol Methods Protoc Year: 2022 Document Type: Article Affiliation country: Biomethods

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Cohort study / Observational study / Prognostic study Language: English Journal: Biol Methods Protoc Year: 2022 Document Type: Article Affiliation country: Biomethods