Sophie Charlotte Niestroj, Sarah Steden, Maren Boecker, Edward Stuart Brodkin, Kerstin Konrad
The Development and Validation of the First German Open Scale of Social Information Processing
Psychopathology
construct validate best predictive model 28day death risk patients septic shock based different supervised machine learning algorithms patients septic shock meeting sepsis3 criteria selected medical information mart intensive careiv v20 mimiciv v20 according principle random allocation 70 patients used training set 30 validation set relevant predictive variables extracted three aspects demographic characteristics basic vital signs serum indicators within 24 hours intensive care unit icu admission complications possibly affecting indicators functional scoring advanced life support predictive efficacy models constructed using five mainstream machine learning algorithms including decision tree classification regression tree cart random forest rf support vector machine svm linear regression lr super learner sl combined cart rf extreme gradient boosting xgboost 28day death patients septic shock compared best algorithm model selected optimal predictive variables determined intersecting results lasso regression rf xgboost algorithms predictive model constructed predictive efficacy model validated drawing receiver operator characteristic curve roc curve accuracy model assessed using calibration curves practicality model verified decision curve analysis dca total 3 295 patients septic shock included 2 164 surviving 1 131 dying within 28 days resulting mortality 3432 2 307 training set 792 deaths within 28 days mortality 3433 988 validation set 339 deaths within 28 days mortality 3431 five machine learning models established based training set data including variables three aspects area roc curve auc rf svm lr machine learning algorithm models predicting 28day death septic shock patients validation set 0823 95 confidence interval 95ci 07950849 0823 95ci 07960849 0810 95ci 07820838 respectively higher cart algorithm model auc 0750 95ci 07170782 sl algorithm model auc 0756 95ci 07240789 thus three algorithm models determined best algorithm models integrating variables three aspects 16 optimal predictive variables identified intersection lasso regression rf xgboost algorithms including highest ph value highest albumin alb highest body temperature lowest lactic acid lac highest lac highest serum creatinine scr highest ca2 lowest hemoglobin hb lowest white blood cell count wbc age simplified acute physiology score iii saps iii highest wbc acute physiology score iii aps iii lowest na body mass index bmi shortest activated partial thromboplastin time aptt within 24 hours icu admission roc curve analysis showed logistic regression model constructed 16 optimal predictive variables best predictive model auc 0806 95ci 07780835 validation set calibration curve dca curve showed model high accuracy highest net benefit could reach 03 significantly outperforming traditional models based single functional score aps iii score saps iii score sequential organ failure assessment sofa score auc 95ci 0746 07150778 0765 07340796 0625 05890661 respectively logistic regression model constructed using 16 optimal predictive variables including ph value alb body temperature lac scr ca2 hb wbc saps iii score aps iii score na bmi aptt identified best predictive model 28day death risk patients septic shock performance stable high discriminative ability accuracy