Selected article for: "PPV positive predictive value and predictive value"
Author: Chuansheng Zheng; Xianbo Deng; Qing Fu; Qiang Zhou; Jiapei Feng; Hui Ma; Wenyu Liu; Xinggang Wang
Title: Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label
  • Document date: 2020_3_17
    • ID: ll4rxd9p_28
    Snippet: When using the threshold of 0.5 to make COVID-19 detection prediction (i.e., if the probability of COVID-19 was larger than 0.5, the patient was classified as COVID-positive, and vice versa), the algorithm obtained an accuracy of 0.901 with a positive predictive value (PPV) of 0.840 and a negative predictive value (NPV) of 0.982. By varying the probability threshold, we obtained a series of COVID-19 detection accuracy, PPV and NPV in Table 2 . Ou.....
    Document: When using the threshold of 0.5 to make COVID-19 detection prediction (i.e., if the probability of COVID-19 was larger than 0.5, the patient was classified as COVID-positive, and vice versa), the algorithm obtained an accuracy of 0.901 with a positive predictive value (PPV) of 0.840 and a negative predictive value (NPV) of 0.982. By varying the probability threshold, we obtained a series of COVID-19 detection accuracy, PPV and NPV in Table 2 . Our data showed that the COVID-19 prediction accuracy obtained by the DeCoVNet algorithm was higher than 0.9 when the threshold ranged from 0.2 to 0.5. At the threshold setting of 0.5, there were 12 false positive predictions in total and only one false positive prediction by the algorithm in our study, indicating that the algorithm to have a very high negative predictive value.

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