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An interpretable mortality prediction model for COVID-19 patients

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Zhang, HT 
Xiao, Y 
Wang, M 


The sudden increase in COVID-19 cases is putting high pressure on healthcare services worldwide. At this stage, fast, accurate and early clinical assessment of the disease severity is vital. To support decision making and logistical planning in healthcare systems, this study leverages a database of blood samples from 485 infected patients in the region of Wuhan, China, to identify crucial predictive biomarkers of disease mortality. For this purpose, machine learning tools selected three biomarkers that predict the mortality of individual patients more than 10 days in advance with more than 90% accuracy: lactic dehydrogenase (LDH), lymphocyte and high-sensitivity C-reactive protein (hs-CRP). In particular, relatively high levels of LDH alone seem to play a crucial role in distinguishing the vast majority of cases that require immediate medical attention. This finding is consistent with current medical knowledge that high LDH levels are associated with tissue breakdown occurring in various diseases, including pulmonary disorders such as pneumonia. Overall, this Article suggests a simple and operable decision rule to quickly predict patients at the highest risk, allowing them to be prioritized and potentially reducing the mortality rate.



46 Information and Computing Sciences, 40 Engineering, Lung, Coronaviruses, Prevention, Emerging Infectious Diseases, Machine Learning and Artificial Intelligence, Clinical Research, Precision Medicine, Infectious Diseases, Pneumonia & Influenza, Pneumonia, 4.1 Discovery and preclinical testing of markers and technologies, 4.2 Evaluation of markers and technologies, Generic health relevance, 3 Good Health and Well Being

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Nature Machine Intelligence

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Springer Science and Business Media LLC


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