Development and validation of a machine learning approach for automated severity assessment of COVID-19 based on clinical and imaging data: retrospective study

Juan Carlos Quiroz, You-Zhen Feng, Zhong-Yuan Cheng, Dana Rezazadegan, Ping-Kang Chen, Qi-Ting Lin, Long Qian, Xiao-Fang Liu, Shlomo Berkovsky, Enrico Coiera, Lei Song, Xiaoming Qiu, Sidong Liu*, Xiang-Ran Cai

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
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Abstract

Background: COVID-19 has overwhelmed health systems worldwide. It is important to identify severe cases as early as possible, such that resources can be mobilized and treatment can be escalated.

Objective: This study aims to develop a machine learning approach for automated severity assessment of COVID-19 based on clinical and imaging data.

Methods: Clinical data—including demographics, signs, symptoms, comorbidities, and blood test results—and chest computed tomography scans of 346 patients from 2 hospitals in the Hubei Province, China, were used to develop machine learning models for automated severity assessment in diagnosed COVID-19 cases. We compared the predictive power of the clinical and imaging data from multiple machine learning models and further explored the use of four oversampling methods to address the imbalanced classification issue. Features with the highest predictive power were identified using the Shapley Additive Explanations framework.

Results: Imaging features had the strongest impact on the model output, while a combination of clinical and imaging features yielded the best performance overall. The identified predictive features were consistent with those reported previously. Although oversampling yielded mixed results, it achieved the best model performance in our study. Logistic regression models differentiating between mild and severe cases achieved the best performance for clinical features (area under the curve [AUC] 0.848; sensitivity 0.455; specificity 0.906), imaging features (AUC 0.926; sensitivity 0.818; specificity 0.901), and a combination of clinical and imaging features (AUC 0.950; sensitivity 0.764; specificity 0.919). The synthetic minority oversampling method further improved the performance of the model using combined features (AUC 0.960; sensitivity 0.845; specificity 0.929).

Conclusions: Clinical and imaging features can be used for automated severity assessment of COVID-19 and can potentially help triage patients with COVID-19 and prioritize care delivery to those at a higher risk of severe disease.
Original languageEnglish
Article numbere24572
Pages (from-to)1-14
Number of pages14
JournalJMIR Medical Informatics
Volume9
Issue number2
DOIs
Publication statusPublished - 11 Feb 2021

Bibliographical note

Copyright the Author(s) 2021. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • algorithm
  • clinical data
  • clinical features
  • COVID-19
  • CT scans
  • development
  • imaging
  • imbalanced data
  • machine learning
  • oversampling
  • severity assessment
  • validation

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