Predictive study on discharge status of hospitalized patients with acute exacerbation of chronic obstructive pulmonary disease
Objective A machine learning prognosis prediction model was created by innova-tively combining discharge status and length of hospital stay,to accurately predict the prognosis of patients with acute exacerbation of chronic obstructive pulmonary disease(AECOPD).This was done in order to address the issues of difficult to obtain pulmonary function tests and large measurement error.Methods A total of 3 035 inpatients with AECOPD were recruited from the second hospital of Shanxi Medical Uni-versity between October 2011 and May 2020.The outcome variable is whether or not the patient recovered and was discharged within the median length of hospitalization.The prediction model is created using five distinct machine learning models:logistic regression,support vector machine,random forest,Catboost,and multi-layer perceptron.By contrasting evaluation metrics like area under the working characteristic curve(AUROC),the optimal model is determined.In order to verify the decision curve's clinical appli-cability,the best model was used to assess it.Results In comparison to other machine learning models,random forest has the greatest overall prediction performance,with AUC of 0.780,accuracy of 69.69％,precision of 64.50％,recall of 75.18％,Fl score of 69.44％,and Brier score of 18.77％.The decision curve analysis has high clinical value,and the calibration curve is largely compatible with the diagonal.Conclusions The prediction model based on random forest may reliably forecast the prognosis of patients with AECOPD and provide some aid to physicians in evaluation and treatment decision-making when the important indices of the lung function test cannot be acquired.
Chronic obstructive pulmonary diseaseAcute exacerbation periodMachine learn-ingPrediction model
NETL
NSTL
万方数据
