Selected article for: "prediction performance and training set"
Author: Wang, Dandan; Huang, Chencui; Bao, Siyu; Fan, Tingting; Sun, Zhongqi; Wang, Yiqiao; Jiang, Huijie; Wang, Song
Title: Study on the prognosis predictive model of COVID-19 patients based on CT radiomics
  • Cord-id: bq0z45a8
  • Document date: 2021_6_2
    • ID: bq0z45a8
    Snippet: Making timely assessments of disease progression in patients with COVID-19 could help offer the best personalized treatment. The purpose of this study was to explore an effective model to predict the outcome of patients with COVID-19. We retrospectively included 188 patients (124 in the training set and 64 in the test set) diagnosed with COVID-19. Patients were divided into aggravation and improvement groups according to the disease progression. Three kinds of models were established, including
    Document: Making timely assessments of disease progression in patients with COVID-19 could help offer the best personalized treatment. The purpose of this study was to explore an effective model to predict the outcome of patients with COVID-19. We retrospectively included 188 patients (124 in the training set and 64 in the test set) diagnosed with COVID-19. Patients were divided into aggravation and improvement groups according to the disease progression. Three kinds of models were established, including the radiomics, clinical, and combined model. Receiver operating characteristic curves, decision curves, and Delong’s test were used to evaluate and compare the models. Our analysis showed that all the established prediction models had good predictive performance in predicting the progress and outcome of COVID-19.

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