Discovering associations between radiological features and COVID‐19 patients' deterioration

BACKGROUND AND AIMS: Data mining methods are effective and well‐known tools for developing predictive models and extracting useful information from various data of patients. The present study aimed to predict the severity of patients with COVID‐19 by applying the rule mining method using characteristics of medical images. METHODS: This retrospective study has analyzed the radiological data from 104 COVID‐19 hospitalized patients diagnosed with COVID‐19 in a hospital in Iran. A data set containing 75 binary features was generated. Apriori method is utilized for association rule mining on this data set. Only rules with confidence equal to one were generated. The performance of rules is calculated by support, coverage, and lift indexes. RESULTS: Ten rules were extracted with only X‐ray‐related features on cases referred to ICU. The Support and Coverage index of all of these rules was 0.087, and the Lift index of them was 1.58. Thirteen rules were extracted from only CT scan‐related features on cases referred to ICU. The CXR_Pleural effusion feature has appeared in all the rules. The CXR_Left upper zone feature appears in 9 rules out of 10. The Support and Coverage index of all rules was 0.15, and the Lift index of all rules was 1.63. the CT_Adjacent pleura thickening feature has appeared in all rules, and the CT_Right middle lobe appeared in 9 rules out of 13. CONCLUSION: This study could reveal the application and efficacy of CXR and CT scan imaging modalities in predicting ICU admission to a major COVID‐19 infection via data mining methods. The findings of this study could help data scientists, radiologists, and clinicians in the future development and implementation of these methods in similar conditions and timely and appropriately save patients from adverse disease outcomes.