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Open AccessArticle

On the Application of Advanced Machine Learning Methods to Analyze Enhanced, Multimodal Data from Persons Infected with COVID-19

by 1,*,†, 1,2,† and 1
1
Institute for Bioinformatics and Medical Informatics, University of Tübingen, Sand 14, 72076 Tübingen, Germany
2
International Max Planck Research School ‘From Molecules to Organisms’, Max Planck Institute for Developmental Biology, Max-Planck-Ring 5, 72076 Tübingen, Germany
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Computation 2021, 9(1), 4; https://doi.org/10.3390/computation9010004
Received: 8 December 2020 / Revised: 29 December 2020 / Accepted: 1 January 2021 / Published: 7 January 2021
(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))
The current COVID-19 pandemic, caused by the rapid worldwide spread of the SARS-CoV-2 virus, is having severe consequences for human health and the world economy. The virus affects different individuals differently, with many infected patients showing only mild symptoms, and others showing critical illness. To lessen the impact of the epidemic, one problem is to determine which factors play an important role in a patient’s progression of the disease. Here, we construct an enhanced COVID-19 structured dataset from more than one source, using natural language processing to add local weather conditions and country-specific research sentiment. The enhanced structured dataset contains 301,363 samples and 43 features, and we applied both machine learning algorithms and deep learning algorithms on it so as to forecast patient’s survival probability. In addition, we import alignment sequence data to improve the performance of the model. Application of Extreme Gradient Boosting (XGBoost) on the enhanced structured dataset achieves 97% accuracy in predicting patient’s survival; with climatic factors, and then age, showing the most importance. Similarly, the application of a Multi-Layer Perceptron (MLP) achieves 98% accuracy. This work suggests that enhancing the available data, mostly basic information on patients, so as to include additional, potentially important features, such as weather conditions, is useful. The explored models suggest that textual weather descriptions can improve outcome forecast. View Full-Text
Keywords: COVID-19; machine learning; deep learning; NLP; weather; sentiment analysis COVID-19; machine learning; deep learning; NLP; weather; sentiment analysis
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MDPI and ACS Style

Zeng, W.; Gautam, A.; Huson, D.H. On the Application of Advanced Machine Learning Methods to Analyze Enhanced, Multimodal Data from Persons Infected with COVID-19. Computation 2021, 9, 4. https://doi.org/10.3390/computation9010004

AMA Style

Zeng W, Gautam A, Huson DH. On the Application of Advanced Machine Learning Methods to Analyze Enhanced, Multimodal Data from Persons Infected with COVID-19. Computation. 2021; 9(1):4. https://doi.org/10.3390/computation9010004

Chicago/Turabian Style

Zeng, Wenhuan; Gautam, Anupam; Huson, Daniel H. 2021. "On the Application of Advanced Machine Learning Methods to Analyze Enhanced, Multimodal Data from Persons Infected with COVID-19" Computation 9, no. 1: 4. https://doi.org/10.3390/computation9010004

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