Using machine learning of clinical data to diagnose COVID-19: a systematic review and meta-analysis

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

Dataset

Description

Abstract 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.
Date made available1 Jan 2020
PublisherFigshare - Springer

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