Thorsten Langer, Danny Jazmati, Ole Jung, Christian Schulz, Martin W Schnell
Medical Students' Development of Ethical Judgment - Exploring the Learners' Perspectives using a mixed methods approach
GMS journal for medical education
introduction known patients immuneabnormal copregnancies higher risk adverse pregnancy outcomes traditional pregnancy risk management systems poor prediction abilities adverse pregnancy outcomes patients many limitations clinical application study use machine learning screen highrisk factors miscarriage develop miscarriage risk prediction model patients immuneabnormal pregnancies model aims provide adjunctive tool clinical identification patients high risk miscarriage allow active intervention reduce adverse pregnancy outcomes methods patients immuneabnormal pregnancies attending sichuan provincial peoples hospital collected electronic medical records emr data divided training set test set 82 ratio comparisons made evaluate performance traditional pregnancy risk assessment tools clinical applications analysis involved assessing costbenefit clinical treatment evaluating models performance determining economic value data sampling methods feature screening machine learning algorithms utilized develop predictive models models internally validated using 10fold crossvalidation training set externally validated using bootstrapping test set model performance assessed area characteristic curve auc based best parameters predictive model miscarriage risk developed shapley additive expansion shap method used assess best model feature contribution results total 565 patients included study machine learningbased models predicting risk miscarriage patients immuneabnormal pregnancies twentyeight risk warning models developed predictive model constructed using xgboost demonstrated best performance auc 09209 shap analysis best model highlighted total number medications well use aspirin low molecular weight heparin significant influencing factors implementation pregnancy risk scoring rules resulted accuracy precision f1 scores 03009 01663 02852 respectively economic evaluation showed saving 74858657 due model conclusion predictive model developed study performed well estimating risk miscarriage patients immuneabnormal pregnancies findings model interpretation identified total number medications use medications pregnancy key factors early warning model miscarriage risk provides important basis early risk assessment intervention immuneabnormal pregnancies predictive model developed study demonstrated better risk prediction performance pregnancy risk management system prms also demonstrated economic value therefore miscarriage risk prediction patients immuneabnormal pregnancies may costeffective management method