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Development and external evaluation of predictions models for mortality of COVID-19 patients using machine learning

Simin Li1, Yulan Lin2, Tong Zhu1

  • 1Yidu Cloud Technology Inc., 8F, Health Work, No. 9 Building, No. 35 of Huayuan North Road, Haidian District, Beijing, 100089 China.

Neural Computing & Applications
|January 11, 2021
PubMed

Insights

The Gradient Boosting Decision Tree (GBDT) model shows superior performance in predicting coronavirus disease 2019 (COVID-19) patient mortality compared to logistic regression models. This finding aids in better patient management and resource allocation during the pandemic.

Area of Science:

  • Medical Informatics
  • Machine Learning in Medicine
  • Epidemiology

Background:

  • Coronavirus disease 2019 (COVID-19) poses a significant global health challenge.
  • Accurate mortality prediction is crucial for patient management and healthcare resource allocation.
  • Existing predictive models require evaluation and comparison for optimal clinical utility.

Purpose of the Study:

  • To develop and compare machine learning models for predicting COVID-19 patient mortality.
  • To evaluate the performance of Gradient Boosting Decision Tree (GBDT) and logistic regression (LR) models.
  • To identify key clinical factors influencing COVID-19 mortality prediction.

Main Methods:

  • Clinical data from 2924 COVID-19 patients in Wuhan, China (January 18 - March 29, 2020) were analyzed.
  • Gradient Boosting Decision Tree (GBDT), logistic regression (LR), and a simplified LR model (LR-5) were constructed.
  • Model performance was assessed using fivefold cross-validation, calculating Area Under Curve (AUC), accuracy, Positive Predictive Value (PPV), and Negative Predictive Value (NPV).

Main Results:

  • The GBDT model achieved the highest fivefold AUC (0.941), outperforming LR (0.928) and LR-5 (0.913).
  • GBDT demonstrated superior diagnostic accuracy (0.889) and sensitivity (0.899) compared to LR models.
  • External validation identified leukomonocyte percentage, urea, age, and SpO2 as significant predictors in a separate cohort.

Conclusions:

  • The GBDT model offers enhanced accuracy for predicting COVID-19 mortality compared to traditional LR models.
  • Machine learning approaches, particularly GBDT, show promise for improving prognostic assessments in infectious diseases.
  • The study highlights the importance of robust model evaluation for clinical decision support systems.

Keywords:
COVID-19ChinaMachine learningMortalityPrediction

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