Machine learning: a predication model of outcome of SARS-CoV-2 pneumonia [post]

Xiaoming Li, Gang Wu, Shuchang Zhou, Yujin Wang
2020 unpublished
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in thousands of deaths in the world. Information about prediction model of prognosis of SARS-CoV-2 infection is scarce. We used machine learning for processing laboratory findings of 110 patients with SARS-CoV-2 pneumonia (including 51 non-survivors and 59 discharged patients). The maximum relevance minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator (LASSO) logistic regression
more » ... ogistic regression model were used for selection of laboratory features. Seven laboratory features selected by machine learning were: prothrombin activity, urea, white blood cell, interleukin-2 receptor, indirect bilirubin, myoglobin, and fibrinogen degradation products. The signature constructed using the seven features had 98% [93%, 100%] sensitivity and 91% [84%, 99%] specificity in predicating outcome of SARS-CoV-2 pneumonia. Thus it is feasible to establish an accurate prediction model of outcome of SARS-CoV-2 pneumonia with machine learning.
doi:10.21203/rs.3.rs-23196/v1 fatcat:owtpumy4gfcvjkbmjerywearjm