Purpose: This study aimed to develop a digital multimedia-based educational intervention for hematopoietic stem cell (HSC) donors and to evaluate its effects on anxiety, knowledge, attitudes toward hematopoietic stem cell donation, and educational satisfaction. Methods: This quasi-experimental study employed a nonequivalent control group non-synchronized pretest-posttest design. Participants were 60 HSC donors admitted to a general hospital in Seoul, Republic of Korea, between April and December 2024. The participants were assigned to an experimental group (n=30) and a control group (n=30). The experimental group received a digital multimediabased educational intervention in addition to the usual education using a standardized handout, whereas the control group received the usual education only. The intervention was provided in three sessions: on the day of admission (20 minutes), on the day of stem cell collection (10 minutes), and on the day of discharge (10 minutes). Data were collected at admission and discharge using structured questionnaires. Anxiety was analyzed using generalized estimating equations (GEE), knowledge and attitudes using mixed ANOVA, and educational satisfaction using an independent t-test. Results: The experimental group showed significantly lower anxiety (p=.048) and higher knowledge levels (p=.005) than the control group. No significant differences were found between the groups in attitudes toward donation or educational satisfaction. Conclusion The digital multimedia-based educational intervention was effective in reducing anxiety and improving knowledge among HSC donors. This intervention may enhance donors’ understanding of the donation process and support a more positive donation experience. Further multicenter studies with larger samples and longitudinal follow-up measurements are needed to verify the long-term effects and generalizability of the intervention.

Objective: Research specifically targeting suicidal ideation (SI) in middle-aged populations remains limited. This study aimed to predict future and concurrent SI in middle-aged Korean adults by applying four machine learning (ML) models to a nationally representative longitudinal dataset. Methods: We analyzed data from 8,992 individuals aged 40–64 years who participated in the Korea Welfare Panel Study from the 7th (2011) to the 18th (2022) waves. Four ML algorithms were employed to develop the predictive models. The SHapley Additive exPlanations method was applied to enhance explainability. Results: Approximately half of the participants’ mean age was 49.3±8.2 years (range, 40–64 years) and 52.2% were male. The average annual SI rate between 2011 and 2022 was 2.8%±1.2%. Predictive performance for future SI was satisfactory, with area under the receiver operating characteristic curve (AUC) values of up to 0.806 (logistic regression, LR). Predictions for concurrent SI demonstrated AUC values of up to 0.907 (LR). Key predictors of future SI included subjective health status, satisfaction with family and spousal relationships, housing environment, and educational attainment. Concurrent SI was strongly associated with immediate stressors such as family violence and income dissatisfaction. Conclusion The ML models demonstrated good-to-excellent predictive performance for SI. These findings emphasize the importance of health, family, and socioeconomic factors, alongside mental health indicators in the prevention of SI among middle-aged adults. Building on these findings, tailored intervention strategies that comprehensively address multidimensional risk factors are essential for effective SI prevention.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Background: Acute deterioration of patients in general wards often leads to major adverse events (MAEs), including unplanned intensive care unit transfers, cardiac arrest, or death. Traditional early warning scores (EWSs) have shown limited predictive accuracy, with frequent false positives. We conducted a prospective observational external validation study of an artificial intelligence (AI)-based EWS, the VitalCare - Major Adverse Event Score (VC-MAES), at a tertiary medical center in the Republic of Korea. Methods: Adult patients from general wards, including internal medicine (IM) and obstetrics and gynecology (OBGYN)—the latter were rarely investigated in prior AI-based EWS studies—were included. The VC-MAES predictions were compared with National Early Warning Score (NEWS) and Modified Early Warning Score (MEWS) predictions using the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and logistic regression for baseline EWS values. False-positives per true positive (FPpTP) were assessed based on the power threshold. Results: Of 6,039 encounters, 217 (3.6%) had MAEs (IM: 9.5%, OBGYN: 0.26%). Six hours prior to MAEs, the VC-MAES achieved an AUROC of 0.918 and an AUPRC of 0.352, including the OBGYN subgroup (AUROC, 0.964; AUPRC, 0.388), outperforming the NEWS (0.797 and 0.124) and MEWS (0.722 and 0.079). The FPpTP was reduced by up to 71%. Baseline VC-MAES was strongly associated with MAEs (P<0.001). Conclusions The VC-MAES significantly outperformed traditional EWSs in predicting adverse events in general ward patients. The robust performance and lower FPpTP suggest that broader adoption of the VC-MAES may improve clinical efficiency and resource allocation in general wards.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Purpose: Depression and frailty are common health problems that occur separately or simultaneously in later life. The two syndromes are correlated, but they need to be distinguished to promote successful aging. Previous studies have examined the reciprocal relationship between depression and frailty, but there are limitations in the methods or statistical analysis. This study aims to confirm the potential prospective bidirectional and causal relationship between depression and frailty. Methods: We used data from 887 older adults aged 70 to 84 from the Korean Frailty and Aging Cohort Study (KFACS) in 2016, 2018, and 2020 (3 waves). We separated the within-individual process from the stable between-individual differences using the random intercepts cross-lagged panel model. Results: Significant bidirectional causal effects were observed in 2 paths. Older adults with higher depression than their within-person average at T1 had a higher risk of frailty at T2 (β=.22, p=.008). Subsequently, older adults with higher-than-average frailty scores at T2 showed higher depression at T3 (β=.14, p=.010). Autoregressive effects were only significant from T2 to T3 for both constructs (Depression: β=.16, p=.044; Frailty: β=.13, p=.028). At the between-person level, the correlation was significant between the random intercepts between depression and frailty (β=.47, p<.001). Conclusions We find that depressed older adults have an increased risk of frailty, which contributes to the onset of depression and the maintenance of frailty. Therefore, interventions for each condition may prevent the entry and worsening of the other condition, as well as prevent comorbidity.

Purpose: This study was a scoping review designed to identify research trends in loneliness and health targeting for domestic and foreign nurses. Methods: The methodological framework was based on a previous work by Arksey and O’Malley. The studies reviewed were found through electronic databases, such as RISS, PubMed, and CINAHL. The period of the data was from January 2000 to December 2021. Results: The 6 studies were reviewed. The loneliness of nurses was found to be experienced when the frequency of social interaction with leaders and colleagues was low, when missing camaraderie and sense of belonging, when work felt meaningless, and when social support level was low. Additionally, loneliness raised levels of depression, anxiety, and stress level while lowered well-being level, and that was shown to have harmful effects for health, such as burnout and fatigue. Also, it was found that loneliness lowered nurses’ job satisfaction and increased their turnover rate. Conclusion Further development of nurses’ loneliness management program is needed. Also, it is suggested that the additional studies to verify causal relationship and mechanism between loneliness and health are required.