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• W3093365120 abstract "Black-box nature hinders the deployment of many high-accuracy models in medical diagnosis. It is risky to put one's life in the hands of models that medical researchers do not trust. However, to understand the mechanism of a new virus, such as COVID-19, machine learning models may catch important symptoms that medical practitioners do not notice due to the surge of infected patients during a pandemic. In this work, the interpretation of machine learning models reveals that a high C-reactive protein (CRP) corresponds to severe infection, and severe patients usually go through a cardiac injury, which is consistent with well-established medical knowledge. Additionally, through the interpretation of machine learning models, we find phlegm and diarrhea are two important symptoms, without which indicate a high risk of turning severe. These two symptoms are not recognized at the early stage of the outbreak, whereas our findings are corroborated by later autopsies of COVID-19 patients. We find patients with a high N-terminal pro B-type natriuretic peptide (NTproBNP) have a significantly increased risk of death which does not receive much attention initially but proves true by the following-up study. Thus, we suggest interpreting machine learning models can offer help to diagnosis at the early stage of an outbreak." @default.
• W3093365120 created "2020-10-22" @default.
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• W3093365120 date "2020-09-30" @default.
• W3093365120 modified "2023-09-23" @default.
• W3093365120 title "Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction Task" @default.
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• W3093365120 hasPublicationYear "2020" @default.
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