Christian Wrede, Annemarie Braakman-Jansen, Lisette van Gemert-Pijnen
Understanding acceptance of contactless monitoring technology in home-based dementia care: a cross-sectional survey study among informal caregivers
Frontiers in digital health
major depression disorder mdd forms common psychiatric comorbidity among patients narcolepsy type 1 nt1 yet impact patients nt1 often overlooked neurologists currently lack effective methods accurately predicting mdd patients nt1 study utilized machine learning ml algorithms identify critical variables developed prediction model predicting mdd patients nt1 study included 267 nt1 patients four sleep centers diagnosis comorbid mdd based diagnostic statistical manual mental disorders fifth edition dsm5 ml models including six full models six compact models developed using training set performance models compared testing set optimal model evaluated testing set various evaluation metrics area receiver operating curve auc precisionrecall pr curve calibration curve employed assess compare performance ml models model interpretability demonstrated using shap testing set logistic regression lg model demonstrated superior performance compared ml models based evaluation metrics auc pr curve calibration curve top eight features used lg model ranked feature importance included social impact scale sis score narcolepsy severity scale nss score total sleep time body mass index bmi education years age onset sleep efficiency sleep latency study yielded straightforward practical ml model early identification mdd patients nt1 webbased tool clinical applications developed deserves verification diverse clinical settings