Dataset Information

Machine learning based early warning system enables accurate mortality risk prediction for COVID-19.

ABSTRACT: Soaring cases of coronavirus disease (COVID-19) are pummeling the global health system. Overwhelmed health facilities have endeavored to mitigate the pandemic, but mortality of COVID-19 continues to increase. Here, we present a mortality risk prediction model for COVID-19 (MRPMC) that uses patients' clinical data on admission to stratify patients by mortality risk, which enables prediction of physiological deterioration and death up to 20 days in advance. This ensemble model is built using four machine learning methods including Logistic Regression, Support Vector Machine, Gradient Boosted Decision Tree, and Neural Network. We validate MRPMC in an internal validation cohort and two external validation cohorts, where it achieves an AUC of 0.9621 (95% CI: 0.9464-0.9778), 0.9760 (0.9613-0.9906), and 0.9246 (0.8763-0.9729), respectively. This model enables expeditious and accurate mortality risk stratification of patients with COVID-19, and potentially facilitates more responsive health systems that are conducive to high risk COVID-19 patients.

SUBMITTER: Gao Y

PROVIDER: S-EPMC7538910 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Machine learning based early warning system enables accurate mortality risk prediction for COVID-19.

Gao Yue Y Cai Guang-Yao GY Fang Wei W Li Hua-Yi HY Wang Si-Yuan SY Chen Lingxi L Yu Yang Y Liu Dan D Xu Sen S Cui Peng-Fei PF Zeng Shao-Qing SQ Feng Xin-Xia XX Yu Rui-Di RD Wang Ya Y Yuan Yuan Y Jiao Xiao-Fei XF Chi Jian-Hua JH Liu Jia-Hao JH Li Ru-Yuan RY Zheng Xu X Song Chun-Yan CY Jin Ning N Gong Wen-Jian WJ Liu Xing-Yu XY Huang Lei L Tian Xun X Li Lin L Xing Hui H Ma Ding D Li Chun-Rui CR Ye Fei F Gao Qing-Lei QL

Nature communications 20201006 1

Soaring cases of coronavirus disease (COVID-19) are pummeling the global health system. Overwhelmed health facilities have endeavored to mitigate the pandemic, but mortality of COVID-19 continues to increase. Here, we present a mortality risk prediction model for COVID-19 (MRPMC) that uses patients' clinical data on admission to stratify patients by mortality risk, which enables prediction of physiological deterioration and death up to 20 days in advance. This ensemble model is built using four ...[more]

PMID: 33024092

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Project description:Many countries have attempted to mitigate and manage issues related to harmful algal blooms (HABs) by monitoring and predicting their occurrence. The infrequency and duration of HABs occurrence pose the challenge of data imbalance when constructing machine learning models for their prediction. Furthermore, the appropriate selection of input variables is a significant issue because of the complexities between the input and output variables. Therefore, the objective of this study was to improve the predictive performance of HABs using feature selection and data resampling. Data resampling was used to address the imbalance in the minority class data. Two machine learning models were constructed to predict algal alert levels using 10 years of meteorological, hydrodynamic, and water quality data. The improvement in model accuracy due to changes in resampling methods was more noticeable than the improvement in model accuracy due to changes in feature selection methods. Models constructed using combinations of original and synthetic data across all resampling methods demonstrated higher prediction performance for the caution level (L-1) and warning level (L-2) than models constructed using the original data. In particular, the optimal artificial neural network and random forest models constructed using combinations of original and synthetic data showed significantly improved prediction accuracy for L-1 and L-2, representing the transition from normal to bloom formation states in the training and testing steps. The test results of the optimal RF model using the original data indicated prediction accuracies of 98.8% for L0, 50.0% for L1, and 50.0% for L2. In contrast, the optimal random forest model using the Synthetic Minority Oversampling Technique-Edited Nearest Neighbor (ENN) sampling method achieved accuracies of 85.0% for L0, 85.7% for L1, and 100% for L2. Therefore, applying synthetic data can address the imbalance in the observed data and improve the detection performance of machine learning models. Reliable predictions using improved models can support the design of management practices to mitigate HABs in reservoirs and ultimately ensure safe and clean water resources.

Dataset Information

Machine learning based early warning system enables accurate mortality risk prediction for COVID-19.

Publications

Machine learning based early warning system enables accurate mortality risk prediction for COVID-19.

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