Selected article for: "negative sample and positive sample"
Author: Felipe Soares; Aline Villavicencio; Michel Jose Anzanello; Flavio Sanson Fogliatto; Marco Idiart; Mark Stevenson
Title: A novel high specificity COVID-19 screening method based on simple blood exams and artificial intelligence
  • Document date: 2020_4_14
    • ID: 2s5xd1oc_37
    Snippet: To develop the ER-CoV model we trained 10 SVM-based SMOTEBoost models. Its initial prediction corresponds to the average probability from all 10 models: if that probability is greater than 0.4, since we aim at taking a conservative threshold to minimize false negatives, the sample is classified as "positive"; otherwise, the model predicts the sample as "negative". Details about model training are provided in the supplementary material, together w.....
    Document: To develop the ER-CoV model we trained 10 SVM-based SMOTEBoost models. Its initial prediction corresponds to the average probability from all 10 models: if that probability is greater than 0.4, since we aim at taking a conservative threshold to minimize false negatives, the sample is classified as "positive"; otherwise, the model predicts the sample as "negative". Details about model training are provided in the supplementary material, together with the source code. Incremental developments will be updated at https://github.com/soares-f/ER-CoV.

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