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In BMC medical informatics and decision making ; h5-index 38.0

BACKGROUND : The recent Coronavirus Disease 2019 (COVID-19) pandemic has placed severe stress on healthcare systems worldwide, which is amplified by the critical shortage of COVID-19 tests.

METHODS : In this study, we propose to generate a more accurate diagnosis model of COVID-19 based on patient symptoms and routine test results by applying machine learning to reanalyzing COVID-19 data from 151 published studies. We aim to investigate correlations between clinical variables, cluster COVID-19 patients into subtypes, and generate a computational classification model for discriminating between COVID-19 patients and influenza patients based on clinical variables alone.

RESULTS : We discovered several novel associations between clinical variables, including correlations between being male and having higher levels of serum lymphocytes and neutrophils. We found that COVID-19 patients could be clustered into subtypes based on serum levels of immune cells, gender, and reported symptoms. Finally, we trained an XGBoost model to achieve a sensitivity of 92.5% and a specificity of 97.9% in discriminating COVID-19 patients from influenza patients.

CONCLUSIONS : We demonstrated that computational methods trained on large clinical datasets could yield ever more accurate COVID-19 diagnostic models to mitigate the impact of lack of testing. We also presented previously unknown COVID-19 clinical variable correlations and clinical subgroups.

Li Wei Tse, Ma Jiayan, Shende Neil, Castaneda Grant, Chakladar Jaideep, Tsai Joseph C, Apostol Lauren, Honda Christine O, Xu Jingyue, Wong Lindsay M, Zhang Tianyi, Lee Abby, Gnanasekar Aditi, Honda Thomas K, Kuo Selena Z, Yu Michael Andrew, Chang Eric Y, Rajasekaran Mahadevan Raj, Ongkeko Weg M


COVID-19, Diagnostic model, Machine learning