Selected article for: "absolute shrinkage and logistic lasso"
Author: Jiao Gong; Jingyi Ou; Xueping Qiu; Yusheng Jie; Yaqiong Chen; Lianxiong Yuan; Jing Cao; Mingkai Tan; Wenxiong Xu; Fang Zheng; Yaling Shi; Bo Hu
Title: A Tool to Early Predict Severe 2019-Novel Coronavirus Pneumonia (COVID-19) : A Multicenter Study using the Risk Nomogram in Wuhan and Guangdong, China
  • Document date: 2020_3_20
    • ID: 51b7hss1_3
    Snippet: China were included retrospectively. All patients with non-severe COVID-19 during hospitalization were followed for more than 15 days after admission. Patients who deteriorated to severe or critical COVID-19 and patients who kept non-severe state were assigned to the severe and non-severe group, respectively. We compared the demographic, clinical, and laboratory data between severe and non-severe group. Based on baseline data, least absolute shri.....
    Document: China were included retrospectively. All patients with non-severe COVID-19 during hospitalization were followed for more than 15 days after admission. Patients who deteriorated to severe or critical COVID-19 and patients who kept non-severe state were assigned to the severe and non-severe group, respectively. We compared the demographic, clinical, and laboratory data between severe and non-severe group. Based on baseline data, least absolute shrinkage and selection operator (LASSO) algorithm and logistic regression model were used to construct a nomogram for risk prediction in the train cohort. The predictive accuracy and discriminative ability of nomogram were evaluated by area under the curve (AUC) and calibration curve.

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