Clinical decision support systems (CDSSs) enable users to make decisions based on clinical data from electronic medical records, facilitating personalized precision medicine treatments. A digital twin (DT) approach enables the interoperability between physical and virtual environments through data analysis using machine learning (ML). By combining DT with the prostate cancer (PCa) process, it is possible to predict cancer prognosis. In this study, we propose a DT-based prediction model for clinical decision-making in the PCa process. Pathology and biochemical recurrence (BCR) were predicted with ML using data from a clinical data warehouse and the PCa process. The DT model was developed using data from 404 patients. The BCR prediction accuracy increased according to the amount of data used, and reached as high as 96.25% when all data were used. The proposed DT-based predictive model can help provide a clinical decision support system for PCa. Further, it can be used to improve medical processes, promote health, and reduce medical costs and problems.

Prostate cancer is the fourth most common cancer affecting South Korean males, and the biochemical recurrence (BCR) of prostate cancer occurs in approximately 25% of patients five years after radical prostatectomy. The ability to predict BCR would help clinicians and patients to make better treatment decisions. Therefore, in this study, we have proposed a web-based clinical decision support system that predicts the BCR of prostate cancer in Korean patients. The data were obtained from the Korean Prostate Cancer Registry (KPCR) database, which contained information about 7394 patients with prostate cancer who were treated at one of the six major medical institutions in South Korea between May 2001 and December 2014. We tested 13 prediction models and selected the gradient boosting classifier because it demonstrated excellent prediction performance. Using this model, we were able to create a web application and once clinical data from patients were entered, the three- and five-year post-surgery BCR predictions could be extracted. We developed a clinical decision support system to provide a prostate cancer BCR predictive function to facilitate postoperative follow-up and clinical management. This system will help clinicians develop a strategic approach for prostate cancer treatment by predicting the likelihood of prostate cancer recurrence.

We developed an mHealth application that can help alleviate the problematic use of smartphones and allied psychological symptoms. This study observed the change in patterns of users’ problematic smartphone use, depression, and anxiety while using the mHealth application. We conducted this study from 9 January to 10 April 2019. The Korean Smartphone Addiction Proneness Scale for Adults, Generalized Anxiety Disorder Scale, and the Patient Health Questionnaire were measured at week 0, 8, 12. A post hoc test of Repeated Measurement Anova analysis and Linear Mixed Model analysis were used. Overall, 190 participants were allocated into two groups. Sixty-six were in the smartphone control-use group and 124 were in the problematic-use group. The study elucidated the difference between the two groups in terms of problematic smartphone use and depression and anxiety after 13 weeks of using the mHealth application. This study showed the use of the mHealth application reducing problematic smartphone use scores and negative symptoms such as anxiety and depression in the PSU group. The results can be used as the basis for similar qualitative studies to further resolve the problematic use of smartphones.