Abstract
With severe acute respiratory syndrome coronavirus 2 spreading globally and causing 2019 coronavirus disease(COVID-19),a challenge that we unprepared for was about how to optimally plan and distribute limited top-medical resources for patients in need of urgent care.To address this challenge,physicians desperately needed a scientific tool to methodically differentiate between cases with varying severity.In this study,the unique data of COVID-19 intensive care unit(ICU)patients provided by the national medical team in Wuhan were classified into discrete and continuous variable types.All continuous data were discretized using an entropy-based method and transformed into serial information margins,in which each information margin is related to a specific symptom or clinical meaning.Finally,all these native and processed discrete data were used to configure a readable scorecard through logistic regression,which is the desired scientific tool aforementioned.A total of 322 ICU patients(age:[median:64,interquartile range:54-75],males:178[55.28％],and death:72[22.36％])were included in the study.Probabilities of mortality in COVID-19 patients can be evaluated using a scorecard model(calibration slope:1.343,Brier:0.048,Dxy=0.972,and population stability index=0.071),with desired model performances(accuracy=0.948,area under curve=0.99,sensitivity=1,and specificity=0.939).This new model can interpret clinical meanings from complex data,and compare it with existing machine learning methods through a black-box mechanism.This new data-information model answers a critical question of how a computing algorithm produces clinically meaningful results that will help physicians logically allocate medical resources for COVID-19 patients.Notably,this tool has limitations,giving that this research is a retrospective study.Hopefully,this tool will be tested further and optimized for adaptation to similar clinical cases in the future.
基金项目
Scientific and Technological Innovation 2030-"New Generation Artificial Intelligence"Major Project(2021ZD0140406)
National Natural Science Foundation of China(62041201)
NETL
NSTL
万方数据