Objective: Despite lower depression rates in men than in women, men’s suicide rates are significantly higher, suggesting potential gaps in depression screening. Rutz et al. developed the Gotland Male Depression Scale (GMDS), which includes symptoms commonly associated with male depression. This study was conducted to validate the Korean version of the GMDS (K-GMDS). Methods: The K-GMDS, Patient Health Questionnaire-9 (PHQ-9), and outpatient records of 233 new patients at the outpatient psychiatry department of Catholic University Hospital in Daegu from February and May 2022 were retrospectively reviewed. Internal consistency was measured using Cronbach’s α, and external validity was tested by analyzing the scale’s correlation with the PHQ-9. The screening capacity of the K-GMDS was tested based on the receiver operating characteristic (ROC) curve, sensitivity, specificity, and overall accuracy. Results: Of 233 patients, 42.6% (n=98) were classified to the depression group. Cronbach’s α was 0.92, and external validity was established with a Pearson’s correlation coefficient of 0.83 between the total score of the K-GMDS and the PHQ-9. While there were no significant differences in the area under the ROC curve between the K-GMDS and the PHQ-9, the K-GMDS had better sensitivity, specificity, and overall accuracy in screening depressive symptoms among men compared to the PHQ-9. Conclusion The K-GMDS exhibits satisfactory reliability and validity in psychiatric outpatient settings and outperforms the PHQ-9 in screening for depression among men. This study will be useful in developing male depression scales that are currently unavailable in South Korea.

Purpose: Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy. Methods: ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers. Results: CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches. Conclusion Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

Objective: Despite lower depression rates in men than in women, men’s suicide rates are significantly higher, suggesting potential gaps in depression screening. Rutz et al. developed the Gotland Male Depression Scale (GMDS), which includes symptoms commonly associated with male depression. This study was conducted to validate the Korean version of the GMDS (K-GMDS). Methods: The K-GMDS, Patient Health Questionnaire-9 (PHQ-9), and outpatient records of 233 new patients at the outpatient psychiatry department of Catholic University Hospital in Daegu from February and May 2022 were retrospectively reviewed. Internal consistency was measured using Cronbach’s α, and external validity was tested by analyzing the scale’s correlation with the PHQ-9. The screening capacity of the K-GMDS was tested based on the receiver operating characteristic (ROC) curve, sensitivity, specificity, and overall accuracy. Results: Of 233 patients, 42.6% (n=98) were classified to the depression group. Cronbach’s α was 0.92, and external validity was established with a Pearson’s correlation coefficient of 0.83 between the total score of the K-GMDS and the PHQ-9. While there were no significant differences in the area under the ROC curve between the K-GMDS and the PHQ-9, the K-GMDS had better sensitivity, specificity, and overall accuracy in screening depressive symptoms among men compared to the PHQ-9. Conclusion The K-GMDS exhibits satisfactory reliability and validity in psychiatric outpatient settings and outperforms the PHQ-9 in screening for depression among men. This study will be useful in developing male depression scales that are currently unavailable in South Korea.

Purpose: Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy. Methods: ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers. Results: CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches. Conclusion Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

Objective: Despite lower depression rates in men than in women, men’s suicide rates are significantly higher, suggesting potential gaps in depression screening. Rutz et al. developed the Gotland Male Depression Scale (GMDS), which includes symptoms commonly associated with male depression. This study was conducted to validate the Korean version of the GMDS (K-GMDS). Methods: The K-GMDS, Patient Health Questionnaire-9 (PHQ-9), and outpatient records of 233 new patients at the outpatient psychiatry department of Catholic University Hospital in Daegu from February and May 2022 were retrospectively reviewed. Internal consistency was measured using Cronbach’s α, and external validity was tested by analyzing the scale’s correlation with the PHQ-9. The screening capacity of the K-GMDS was tested based on the receiver operating characteristic (ROC) curve, sensitivity, specificity, and overall accuracy. Results: Of 233 patients, 42.6% (n=98) were classified to the depression group. Cronbach’s α was 0.92, and external validity was established with a Pearson’s correlation coefficient of 0.83 between the total score of the K-GMDS and the PHQ-9. While there were no significant differences in the area under the ROC curve between the K-GMDS and the PHQ-9, the K-GMDS had better sensitivity, specificity, and overall accuracy in screening depressive symptoms among men compared to the PHQ-9. Conclusion The K-GMDS exhibits satisfactory reliability and validity in psychiatric outpatient settings and outperforms the PHQ-9 in screening for depression among men. This study will be useful in developing male depression scales that are currently unavailable in South Korea.

Purpose: Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy. Methods: ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers. Results: CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches. Conclusion Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

Objective: Despite lower depression rates in men than in women, men’s suicide rates are significantly higher, suggesting potential gaps in depression screening. Rutz et al. developed the Gotland Male Depression Scale (GMDS), which includes symptoms commonly associated with male depression. This study was conducted to validate the Korean version of the GMDS (K-GMDS). Methods: The K-GMDS, Patient Health Questionnaire-9 (PHQ-9), and outpatient records of 233 new patients at the outpatient psychiatry department of Catholic University Hospital in Daegu from February and May 2022 were retrospectively reviewed. Internal consistency was measured using Cronbach’s α, and external validity was tested by analyzing the scale’s correlation with the PHQ-9. The screening capacity of the K-GMDS was tested based on the receiver operating characteristic (ROC) curve, sensitivity, specificity, and overall accuracy. Results: Of 233 patients, 42.6% (n=98) were classified to the depression group. Cronbach’s α was 0.92, and external validity was established with a Pearson’s correlation coefficient of 0.83 between the total score of the K-GMDS and the PHQ-9. While there were no significant differences in the area under the ROC curve between the K-GMDS and the PHQ-9, the K-GMDS had better sensitivity, specificity, and overall accuracy in screening depressive symptoms among men compared to the PHQ-9. Conclusion The K-GMDS exhibits satisfactory reliability and validity in psychiatric outpatient settings and outperforms the PHQ-9 in screening for depression among men. This study will be useful in developing male depression scales that are currently unavailable in South Korea.

Purpose: Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy. Methods: ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers. Results: CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches. Conclusion Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

Objective: Despite lower depression rates in men than in women, men’s suicide rates are significantly higher, suggesting potential gaps in depression screening. Rutz et al. developed the Gotland Male Depression Scale (GMDS), which includes symptoms commonly associated with male depression. This study was conducted to validate the Korean version of the GMDS (K-GMDS). Methods: The K-GMDS, Patient Health Questionnaire-9 (PHQ-9), and outpatient records of 233 new patients at the outpatient psychiatry department of Catholic University Hospital in Daegu from February and May 2022 were retrospectively reviewed. Internal consistency was measured using Cronbach’s α, and external validity was tested by analyzing the scale’s correlation with the PHQ-9. The screening capacity of the K-GMDS was tested based on the receiver operating characteristic (ROC) curve, sensitivity, specificity, and overall accuracy. Results: Of 233 patients, 42.6% (n=98) were classified to the depression group. Cronbach’s α was 0.92, and external validity was established with a Pearson’s correlation coefficient of 0.83 between the total score of the K-GMDS and the PHQ-9. While there were no significant differences in the area under the ROC curve between the K-GMDS and the PHQ-9, the K-GMDS had better sensitivity, specificity, and overall accuracy in screening depressive symptoms among men compared to the PHQ-9. Conclusion The K-GMDS exhibits satisfactory reliability and validity in psychiatric outpatient settings and outperforms the PHQ-9 in screening for depression among men. This study will be useful in developing male depression scales that are currently unavailable in South Korea.

Purpose: Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy. Methods: ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers. Results: CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches. Conclusion Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.