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Objective: Cognitive decline is often considered an inevitable aspect of aging; however, recent research has identified a subset of older adults known as "superagers" who maintain cognitive abilities comparable to those of younger individuals. Investigating the neurobiological characteristics associated with superior cognitive function in superagers is essential for understanding "successful aging." Evidence suggests that the gut microbiome plays a key role in brain function, forming a bidirectional communication network known as the microbiome-gut-brain axis. Alterations in the gut microbiome have been linked to cognitive aging markers such as oxidative stress and inflammation. This study aims to investigate the unique patterns of the gut microbiome in superagers and to develop machine learning-based predictive models to differentiate superagers from typical agers. Methods: We recruited 161 cognitively unimpaired, community-dwelling volunteers aged 60 years or from dementia prevention centers in Seoul, South Korea. After applying inclusion and exclusion criteria, 115 participants were included in the study. Following the removal of microbiome data outliers, 102 participants, comprising 57 superagers and 45 typical agers, were finally analyzed. Superagers were defined based on memory performance at or above average normative values of middle-aged adults. Gut microbiome data were collected from stool samples, and microbial DNA was extracted and sequenced. Relative abundances of bacterial genera were used as features for model development. We employed the LightGBM algorithm to build predictive models and utilized SHAP analysis for feature importance and interpretability. Results: The predictive model achieved an AUC of 0.832 and accuracy of 0.764 in the training dataset, and an AUC of 0.861 and accuracy of 0.762 in the test dataset. Significant microbiome features for distinguishing superagers included Alistipes, PAC001137_g, PAC001138_g, Leuconostoc, and PAC001115_g. SHAP analysis revealed that higher abundances of certain genera, such as PAC001138_g and PAC001115_g, positively influenced the likelihood of being classified as superagers. Conclusion: Our findings demonstrate the machine learning-based predictive models using gut-microbiome features can differentiate superagers from typical agers with a reasonable performance. [ABSTRACT FROM AUTHOR]

The application of artificial intelligence (AI) algorithms to 12-lead electrocardiogram (ECG) provides promising age prediction models. We explored whether the gap between the pre-procedural AI-ECG age and chronological age can predict atrial fibrillation (AF) recurrence after catheter ablation. We validated a pre-trained residual network-based model for age prediction on four multinational datasets. Then we estimated AI-ECG age using a pre-procedural sinus rhythm ECG among individuals on anti-arrhythmic drugs who underwent de-novo AF catheter ablation from two independent AF ablation cohorts. We categorized the AI-ECG age gap based on the mean absolute error of the AI-ECG age gap obtained from four model validation datasets; aged-ECG (≥10 years) and normal ECG age (<10 years) groups. In the two AF ablation cohorts, aged-ECG was associated with a significantly increased risk of AF recurrence compared to the normal ECG age group. These associations were independent of chronological age or left atrial diameter. In summary, a pre-procedural AI-ECG age has a prognostic value for AF recurrence after catheter ablation. [ABSTRACT FROM AUTHOR]

Applying latent growth mixture modeling (GMM), this study delves into the examination of self-esteem trajectories in a sample of 5,597 older adults over a nine-year period. Four distinct patterns of self-esteem changes have emerged: low, decreasing, increasing, and high. Additionally, the study explores the relationships between each trajectory and various predictors encompassing demographic factors, socioeconomic status, health, and interpersonal relationships. The findings highlight the significance of these factors in predicting the likelihood of an individual following a specific self-esteem trajectory. Notably, maintaining employment, fostering satisfactory social relationships, and being free of frequent depressive feelings emerged as strong predictors for the stability and increase of high self-esteem. Intriguingly, an average or above-average income was unexpectedly associated with lower levels of self-esteem. The study emphasizes the contribution of GMM to advancing aging research. [ABSTRACT FROM AUTHOR]

This study examines whether health-related quality of life (HRQoL) scores differ due to the frequency of walking or physical activity (PA) throughout a week in diabetic patients in Korea. This population-based cross-sectional study used data from the 2018–2021 Korea National Health and Nutrition Examination Survey. The dependent variable was HRQoL scores as measured with EuroQol-5D (EQ-5D). The independent variables were defined as three types of PA: (1) walking; (2) moderate; and (3) vigorous. An estimated population size of 2,376,066 was included in this study. The mean (95% confidence interval (CI)) age of patients was 60.12 years (59.23, 60.81), and 53.0% were female. The mean (95% CI) of EQ-5D was 0.867 (0.857, 0.877). The majority of diabetic patients reported walking daily (39.05%, 95% CI; 36.28–41.81%), while a significant proportion did not engage in moderate (65.45%, 95% CI; 62.79–68.11%) or vigorous (78.38%, 95% CI; 73.02–77.73%) PA at all. After controlling for covariates, EQ-5D scores significantly increased when patients had walked once per week for at least 10 min in the Tobit regression model. The frequency of walking was the most significant predicting factor for better HRQoL in patients with type 2 diabetes. [ABSTRACT FROM AUTHOR]

Aims Although many studies have examined the predictors of medication adherence (MA), further empirical research is required to clarify the best model for predicting MA for older adults with heart failure (HF). Thus, we hypothesized a model in which information (knowledge), motivation (social support and depressive symptoms), and behavioural skills (barriers to self-efficacy) would be associated with MA in patients with HF. Methods and results Using a cross-sectional survey, 153 adults aged ≥ 65 years taking medication for HF were recruited from a university hospital in Korea. Data were collected based on the information–motivation–behavioural skills (IMB) model constructs and MA. In the hypothesized path model, self-efficacy was directly related to MA (β = −0.335, P = 0.006), whereas social support was indirectly related to MA through self-efficacy (β = −0.078, P = 0.027). Depressive symptoms were directly related to MA (β = 0.359, P = 0.004) and indirectly related to MA through self-efficacy (β = 0.141, P = 0.004). The hypothesized MA model showed a good fit for the data. Knowledge, social support, and depressive symptoms accounted for 44.3% of the variance in self-efficacy (P = 0.004). Left ventricular ejection fraction, knowledge, social support, depressive symptoms, and self-efficacy explained 64.4% of the variance in MA (P = 0.004). Conclusion These results confirmed the IMB model's suitability for predicting MA in older adults with HF. These findings may guide and inform intervention programmes designed to alleviate depressive symptoms in older adults with HF and enhance their HF knowledge, social support, and self-efficacy, with the ultimate goal of improving their MA. [ABSTRACT FROM AUTHOR]

A data‐driven model for predicting the surface temperature using neural networks was proposed to alleviate the computational burden of numerical weather prediction (NWP). Our model, named TPTNet uses only 2 m temperature measured at the weather stations of the South Korean Peninsula as input to predict the local temperature at finite forecast hours. The turbulent fluctuation component of the temperature was extracted from the station measurements by separating the climatology component accounting for the yearly and daily variations. The effect of station altitude was then compensated by introducing a potential temperature. The resulting turbulent potential temperature (TPT) data at irregularly distributed stations were used as input for predicting the TPT at forecast hours through three trained networks based on convolutional neural network, Swin Transformer, and a graph neural network. By comparing the prediction performance of our network with that of persistence and NWP, we found that our model can make predictions comparable to NWP for up to 12 hr. Plain Language Summary: We developed a new model called TPTNet to predict local temperatures using only temperature measurements from weather stations in South Korea. This model aims to reduce the heavy computational demands of traditional numerical weather prediction (NWP) methods. By analyzing 20 years of data, we separated the regular temperature patterns that change yearly and daily from the more unpredictable fluctuations. We also adjusted the model for the altitude of each weather station. We trained three different types of neural networks for this purpose: a convolutional neural network, a Swin Transformer, and a graph neural network. When we tested our model with new data from 2020, it made reliable temperature predictions for up to 12 hr, performing as well as, or even better than, traditional NWP methods. This new approach could help improve weather forecasting by making it faster and less computationally intensive. Key Points: Only 2 m temperature data measured at the weather station is used as input for the prediction of temperature at finite forecast hoursTurbulent fluctuation temperature relative to the climatological yearly and daily periodic variations with the altitude adjustment using potential temperature is considered in data‐driven learningFor 12 hr of forecast hour, TPTNet produces forecasts comparable to those of the numerical weather prediction, with the less scattered errors over stations [ABSTRACT FROM AUTHOR]

Purpose: Despite a rapid increase in older men living alone, relatively little is known about their life satisfaction and related factors. Based on Levine's conservation model, this study aimed to explore predictors of life satisfaction among older men living alone in South Korea. Methods: Using logistic regression, a cross-sectional secondary data analysis was conducted among aged men living alone (N=548) from the 2020 National Survey of Older Koreans. Results: The factors associated with a risk of lower life satisfaction in older men living alone were income lower than the standard median income, unemployment, living in rural areas, moderate-high nutritional risk, declined sensory function, depression, and social isolation. Conclusion: To enhance the life satisfaction of older men living alone, customized interventions are required, including comprehensive health assessment with a holistic perspective. Addressing economic stability alongside prevention and multidimensional interventions is needed to mitigate nutritional risks, sensory function decline, depression, and social isolation. Healthcare providers and policymakers in local communities should understand the sociocultural characteristics of older Korean men living alone and provide well-suited formal and informal support programs to address these needs. [ABSTRACT FROM AUTHOR]

Purpose: The purposes of this study were to develop a prediction model for pressure injury using a machine learning algorithm and to integrate it into clinical practice. Methods: This was a retrospective study of tertiary hospitals in Seoul, Korea. It analyzed patients in 12 departments where many pressure injuries occurred, including 8 general wards and 4 intensive care units from January 2018 to May 2022. In total, 182 variables were included in the model development. A pressure injury prediction model was developed using the gradient boosting algorithm, logistic regression, and decision tree methods, and it was compared to the Braden scale. Results: Among the 1,389,660 general ward cases, there were 451 cases of pressure injuries, and among 139,897 intensive care unit cases, there were 297 cases of pressure injuries. Among the tested prediction models, the gradient boosting algorithm showed the highest predictive performance. The area under the receiver operating characteristic curve of the gradient boosting algorithm's pressure injury prediction model in the general ward and intensive care unit was 0.86 (95% confidence interval, 0.83~0.89) and 0.83 (95% confidence interval, 0.79~0.87), respectively. This model was integrated into the electronic health record system to show each patient's probability for pressure injury occurrence, and the risk factors calculated every hour. Conclusion: The prediction model developed using the gradient boosting algorithm exhibited higher performance than the Braden scale. A clinical decision support system that automatically assesses pressure injury risk allows nurses to focus on patients at high risk for pressure injuries without increasing their workload. [ABSTRACT FROM AUTHOR]

Suicide among the older population is a significant public health concern in South Korea. As the older individuals have long considered suicide before committing suicide trials, it is important to analyze the suicidal ideation that precedes the suicide attempt for intervention. In this study, six machine learning algorithms were employed to construct a predictive model for suicidal thinking and identify key variables. A traditional logistic regression analysis was supplementarily conducted to test the robustness of the results of machine learning. All analyses were conducted using a hierarchical approach to compare the model fit of each model in both machine learning and logistic regression. Three models were established for analysis. In Model 1, socioeconomic, residential, and health behavioral factors were incorporated. Model 2 expanded upon Model 1 by integrating physical health status, and Model 3 further incorporated mental health conditions. The results indicated that the gradient boosting algorithm outperformed the other machine learning techniques. Furthermore, the household income quintile was the most important feature in Model 1, followed by subjective health status, oral health, and exercise ability in Model 2, and anxiety and depression in Model 3. These results correspond to those of the hierarchical logistic regression. Notably, economic and residential vulnerabilities are significant factors in the mental health of the older population with higher instances of suicidal thoughts. This hierarchical approach could reveal the potential target population for suicide interventions. [ABSTRACT FROM AUTHOR]

Background: Although endoscopy is commonly used for gastric cancer screening in South Korea, predictive models that integrate endoscopy results are scarce. We aimed to develop a 5-year gastric cancer risk prediction model using endoscopy results as a predictor. Methods: We developed a predictive model using the cohort data of the Kangbuk Samsung Health Study from 2011 to 2019. Among the 260,407 participants aged ≥20 years who did not have any previous history of cancer, 435 cases of gastric cancer were observed. A Cox proportional hazard regression model was used to evaluate the predictors and calculate the 5-year risk of gastric cancer. Harrell's C-statistics and Nam-D'Agostino χ2 test were used to measure the quality of discrimination and calibration ability, respectively. Results: We included age, sex, smoking status, alcohol consumption, family history of cancer, and previous results for endoscopy in the risk prediction model. This model showed sufficient discrimination ability [development cohort: C-Statistics: 0.800, 95% confidence interval (CI) 0.770–0.829; validation cohort: C-Statistics: 0.799, 95% CI 0.743–0.856]. It also performed well with effective calibration (development cohort: χ2 = 13.65, P = 0.135; validation cohort: χ2 = 15.57, P = 0.056). Conclusion: Our prediction model, including young adults, showed good discrimination and calibration. Furthermore, this model considered a fixed time interval of 5 years to predict the risk of developing gastric cancer, considering endoscopic results. Thus, it could be clinically useful, especially for adults with endoscopic results. [ABSTRACT FROM AUTHOR]

Parental actions, such as parent-child communication and parent-teacher consultation about a child's social adjustment, have been addressed as predictors, but not as outcomes of victimization. This study, based on the Bronfenbrenner's social-ecological model, considered them as outcomes as well as predictors of child victimization and examined their longitudinal bi-directional relationship with child victimization. Data were drawn from the Seoul Education Longitudinal Study, where a total of 4005 Korean youth (female = 43.6%, age mean = 12.43, SD = 1.48 in the first wave), and their parents (female = 87%) were surveyed for six waves (when the youth were 7th to 12th grade). Autoregressive cross-lagged analyses revealed that child victimization positively predicted parent-teacher consultation and negatively predicted parent-child communication, and of these strategies, only parent-child communication was a statistically significant negative predictor of subsequent victimization. The results of this study suggest that parents tend to talk with teachers instead of their own children when bullying occurs, but it is ineffective in preventing further victimization. Communicating with one's children, which is a less common reaction, appears to be a better preventative measure. [ABSTRACT FROM AUTHOR]

Aim: As the size of the elderly population gradually increases, musculoskeletal disorders, such as sarcopenia, are increasing. Diagnostic techniques such as X‐rays, computed tomography, and magnetic resonance imaging are used to predict and diagnose sarcopenia, and methods using machine learning are gradually increasing. This study aimed to create a model that can predict sarcopenia using physical characteristics and activity‐related variables without medical diagnostic equipment, such as imaging equipment, for the elderly aged 60 years or older. Methods: A sarcopenia prediction model was constructed using public data obtained from the Korea National Health and Nutrition Examination Survey. Models were built using Logistic Regression, Support Vector Machine (SVM), XGBoost, LightGBM, RandomForest, and Multi‐layer Perceptron Neural Network (MLP) algorithms, and the feature importance of the models trained with the algorithms, except for SVM and MLP, was analyzed. Results: The sarcopenia prediction model built with the LightGBM algorithm achieved the highest test accuracy, of 0.848. In constructing the LightGBM model, physical characteristic variables such as body mass index, weight, and waist circumference showed high importance, and activity‐related variables were also used in constructing the model. Conclusions: The sarcopenia prediction model, which consisted of only physical characteristics and activity‐related factors, showed excellent performance. This model has the potential to assist in the early detection of sarcopenia in the elderly, especially in communities with limited access to medical resources or facilities. Geriatr Gerontol Int 2024; 24: 595–602. [ABSTRACT FROM AUTHOR]

Active migration and globalization have led to increased opportunities for critical care nurses to care for patients from diverse racial and cultural backgrounds. This study thus aimed to identify the individual, interpersonal, and organizational factors affecting cultural competence levels among neonatal intensive care unit (NICU) nurses based on an ecological model. This was a cross-sectional descriptive study that included 135 NICU nurses in South Korea. A hierarchical multiple linear regression analysis was conducted using the proposed ecological model, and a regression model for each of the four subdomains of cultural competence was constructed and compared. NICU nurses' cultural competencies were influenced not only by the "necessity of multicultural education" and "ethnocultural empathy" at the individual level but by the "hospital's readiness and support for cultural competencies" at the organizational level. To promote the cultural competence of nurses in critical care settings, environmental and organizational support should be improved, along with developing strategies that focus on nurses' individual characteristics. It is also necessary to investigate the "intersectionality" of the effects of individual and environmental factors on cultural competence. [ABSTRACT FROM AUTHOR]

This study examines the relationship between cyber violence and cyber sex crimes, specifically focusing on these crimes as systemic issues among adolescents. The research highlights the severe impact of cyber sex crimes, characterized by the non-consensual sharing of sexually explicit content. It examines various factors that may contribute to witnessing cyber sex crimes, including exposure to violent online content, personal experiences of cyber violence (either as a victim or perpetrator), and the role of parental and teacher interventions. Utilizing data from a nationwide survey conducted by the Korea Communications Commission, the study analyzes responses from 9016 adolescents in 2021 and 9693 in 2022. This analysis reveals significant predictors of witnessing cyber sex crimes and examines how perceptions of cyber violence and interventions of authoritative figures may influence adolescents' perception of cyber sex crimes as either systemic or individual issues. With females disproportionately affected, the findings underscore a gendered aspect of cyber violence. Furthermore, these insights suggest that perceiving cyber violence as a serious issue leads to viewing cyber sex crimes as systemic problems necessitating societal intervention. The study advocates for enhanced digital literacy education and systemic changes to protect adolescents from the widespread threats of cyber violence and sex crimes. [ABSTRACT FROM AUTHOR]

Background. Although retaining new nurses is imperative for the future of the nursing profession, it remains a challenging task in the healthcare industry. Understanding the career journey of new graduates as they transition from students to nurses is vital. However, longitudinal studies investigating the factors influencing retention during this period are lacking. Aim. The aim of this study is to identify the influencing factors and develop a longitudinal prediction model for new graduate nurse retention. Methods. A secondary data analysis was conducted using the New Nurse e-Cohort Study dataset from two survey periods, November–December 2020 and February–March 2022. The participants were categorized into either retention or turnover groups based on their turnover experiences. A decision tree based on classification and regression tree (CART) analysis was utilized. Results. Of the total 586 participants, 463 (79%) were in the retention group. The CART model highlighted that new nurses' retention was significantly associated with younger age, higher readiness for practice (clinical problem-solving) during the nursing program, lower transition shock (such as confusion in professional values, loss of social support, and conflicts between theory and practice), and a higher person-environment fit (person-job fit). The predictive accuracy of the CART model was 79.7%. Conclusion. To retain new nurses, nursing educators and hospital managers should collaborate to prepare nursing students for actual practice, offer support during organizational socialization, and foster healthy professional values for competence in the workplace. Implications for Nursing Management. Transforming the educational strategies of nursing programs and hospital management policies is imperative to ultimately enhance the retention of new graduate nurses. [ABSTRACT FROM AUTHOR]

Influenza affects a considerable proportion of the global population each year, and meteorological conditions may have a significant impact on its transmission. In this study, we aimed to develop a prediction model for the number of influenza patients at the national level using satellite images and provide a basis for predicting influenza through satellite image data. We developed an influenza incidence prediction model using satellite images and influenza patient data. We collected satellite images and daily influenza patient data from July 2014 to June 2019 and developed a convolutional long short-term memory (LSTM)–LSTM neural network model. The model with the lowest average of mean absolute error (MAE) was selected. The final model showed a high correlation between the predicted and actual number of influenza patients, with an average MAE of 5.9010 per million population. The model performed best with a 2-week time sequence. We developed a national-level prediction model using satellite images to predict influenza incidence. The model offers the advantage of nationwide analysis. These results may reduce the burden of influenza by enabling timely public health interventions. [ABSTRACT FROM AUTHOR]

Aims Mammography, commonly used for breast cancer screening in women, can also predict cardiovascular disease. We developed mammography-based deep learning models for predicting coronary artery calcium (CAC) scores, an established predictor of coronary events. Methods and results We evaluated a subset of Korean adults who underwent image mammography and CAC computed tomography and randomly selected approximately 80% of the participants as the training dataset, used to develop a convolutional neural network (CNN) to predict detectable CAC. The sensitivity, specificity, area under the receiver operating characteristic curve (AUROC), and overall accuracy of the model's performance were evaluated. The training and validation datasets included 5235 and 1208 women, respectively [mean age, 52.6 (±10.2) years], including non-zero cases (46.8%). The CNN-based deep learning prediction model based on the Resnet18 model showed the best performance. The model was further improved using contrastive learning strategies based on positive and negative samples: sensitivity, 0.764 (95% CI, 0.667–0.830); specificity, 0.652 (95% CI, 0.614–0.710); AUROC, 0.761 (95% CI, 0.742–0.780); and accuracy, 70.8% (95% CI, 68.8–72.4). Moreover, including age and menopausal status in the model further improved its performance (AUROC, 0.776; 95% CI, 0.762–0.790). The Framingham risk score yielded an AUROC of 0.736 (95% CI, 0.712–0.761). Conclusion Mammography-based deep learning models showed promising results for predicting CAC, performing comparably to conventional risk models. This indicates mammography's potential for dual-risk assessment in breast cancer and cardiovascular disease. Further research is necessary to validate these findings in diverse populations, with a particular focus on representation from national breast screening programmes. [ABSTRACT FROM AUTHOR]

Accurate prediction of water quality changes in the water treatment process is an important factor for optimal decision-making process such as the design, operation, and diagnosis of water treatment facilities. This study developed an Artificial Intelligence (AI) algorithm model predicting dissolved organic carbon (DOC) removal and disinfection byproducts formation, and comparatively analyzed existing empirical models and prediction results to examine the applicability of AI algorithm techniques to water quality prediction in the water treatment process. We enhanced empirical models for predicting DOC removal and disinfection byproduct formation in Korea water purification plant. Six AI algorithm techniques were applied and tested using real-world data. All AI algorithm models outperformed the original empirical models in predicting DOC removal and byproduct formation. In terms of the DOC prediction model, multi-layer perceptron (MLP) showed the optimal performance (R² = 0.9795; root mean square error [RMSE] = 0.0365 mg/L). MLP also showed the optimal performance in disinfection byproduct formation prediction (R² = 0.9781; RMSE = 0.0008 mg/L. As a result, the prediction performance of AI algorithm models improved with larger sample sizes. By securing data samples for approximately 1 year, these models were confirmed to outperform empirical models. [ABSTRACT FROM AUTHOR]

Background: Chronic opioid use is associated with problematic opioid use, such as opioid abuse. It is important to develop a prediction model for safe opioid use. In this study, we aimed to develop and validate a risk score model for chronic opioid use in opioid‐naïve, noncancer patients, using data from a nationwide database. Methods: Data from the National Health Insurance Claims Database in the Republic of Korea from 2016 to 2018 were used, and adult, noncancer patients who were started on non‐injectable opioid analgesics (NIOAs) were included. The risk score model was developed using the β coefficient of each variable in the multivariable logistic regression analysis. Results: Overall, 676,676 noncancer patients were started on NIOAs, of which 65,877 (9.7%) were prescribed NIOAs chronically. Age, baseline healthcare utilization, comorbidities, co‐medications, and pattern of first NIOA prescription were identified as risk factors for chronic opioid use. The c‐static for the performance of our risk score model was 0.754 (95% confidence interval, 0.750–0.758). Conclusion: To our knowledge, this is the first tool that can predict chronic opioid use in the Korean population. The model can help physicians examine the risk of chronic opioid use by patients who are started on NIOA. [ABSTRACT FROM AUTHOR]

Background & Aims: Although non‐alcoholic fatty liver disease (NAFLD) is becoming a leading cause of hepatocellular carcinoma (HCC), HCC risk in non‐cirrhotic NAFLD received little attention. We aimed to develop and validate an HCC risk prediction model for non‐cirrhotic NAFLD. Methods: A nationwide cohort of non‐cirrhotic NAFLD patients in Korea was recruited to develop a risk prediction model and validate it internally (n = 409 088). A model using a simplified point system was developed by Cox proportional hazard model. K‐fold cross‐validation assessed the accuracy, discrimination and calibration. The model was validated externally using a hospital cohort from Asan Medical Center (n = 8721). Results: An 11‐point HCC risk prediction model for non‐cirrhotic NAFLD was developed using six independent factors of age, sex, diabetes, obesity, serum alanine aminotransferase level and gamma‐glutamyl transferase level (c‐index 0.75). The average area under receiver operating curves (AUROCs) of the model was 0.72 at 5 years and 0.75 at 10 years. In the external validation cohort, the AUROCs were 0.79 [95% confidence interval [CI], 0.59–0.95] at 5 years and 0.84 (95% CI, 0.73–0.94) at 10 years. The calibration plots showed the expected risks corresponded well with the observed risks. Risk stratification categorized patients into the low (score 0–6), moderate (7, 8) and high (9–11; estimated incidence rate >0.2%/year) risk groups. Conclusions: A novel HCC risk prediction model for non‐cirrhotic NAFLD patients was developed and validated with fair performance. The model is expected to serve as a simple and reliable tool to assess HCC risk and assist precision screening of HCC. [ABSTRACT FROM AUTHOR]

The benefits of incorporating process information in a large-scale assessment with the complex micro-level evidence from the examinees (i.e., process log data) are well documented in the research across large-scale assessments and learning analytics. This study introduces a deep-learning-based approach to predictive modeling of the examinee's performance in sequential, interactive problem-solving tasks from a large-scale assessment of adults' educational competencies. The current methods disambiguate problem-solving behaviors using network analysis to inform the examinee's performance in a series of problem-solving tasks. The unique contribution of this framework lies in the introduction of an "effort-aware" system. The system considers the information regarding the examinee's task-engagement level to accurately predict their task performance. The study demonstrates the potential to introduce a high-performing deep learning model to learning analytics and examinee performance modeling in a large-scale problem-solving task environment collected from the OECD Programme for the International Assessment of Adult Competencies (PIAAC 2012) test in multiple countries, including the United States, South Korea, and the United Kingdom. Our findings indicated a close relationship between the examinee's engagement level and their problem-solving skills as well as the importance of modeling them together to have a better measure of students' problem-solving performance. [ABSTRACT FROM AUTHOR]

Background: Many complex factors contribute to suicide in older adults. The suicidal ideation that precedes suicide is an especially direct predictor of suicide. This study aimed to identify the effects between variables affecting suicidal ideation among older adults using the International Classification of Functioning, Disability and Health (ICF) model and understand the causal relationships to systematize complex factors. Methods: This study used data from 9920 community-dwelling older adults who completed a national survey in 2020 to classify predictors of suicidal ideation (e.g., depression, subjective health status, sociodemographic factors, health factors, social support, instrumental activities of daily living (IADL), and social participation) by using the ICF model. To determine the causal relationship between variables, this study examined significance based on the critical ratio (C.R.) and squared multiple correlation (SMC) by using a path model. Results: Gender, education level, economic level, age, IADL, relationship satisfaction with a child, depression, and the number of chronic diseases significantly affected suicidal ideation, while age, employment status, participation in social groups, formal and informal support, satisfaction with a friend/neighbor relationship, and subjective health status did not significantly influence it. Moreover, depression mediated the relationship between each of these variables and suicidal ideation. Conclusions: It was found that depression was the most direct and mediating factor in suicidal ideation among many factors affecting the suicidal ideation of community-dwelling older adults. Additional studies should be conducted to develop community-level strategies based on these factors and understand causal relationships. [ABSTRACT FROM AUTHOR]

The selection of explanatory variables is important in modeling prediction of changes in species distribution in response to climate change. In this study, we evaluated the importance of variable selection in species distribution models. We compared two different types of models for predicting the distribution of ant species: temperature-only and both temperature and precipitation. Ants were collected at 343 forest sites across South Korea from 2006 through 2009. We used a generalized additive model (GAM) to predict the future distribution of 16 species that showed significant responses to changes in climatic factors (temperature and/or precipitation). Four types of GAMs were constructed: temperature, temperature with interaction of precipitation, temperature and precipitation without interaction, and temperature and precipitation with interaction. Most species displayed similar results between the temperatureonly and the temperature and precipitation models. The results for predicted changes in species richness were different from the temperature-only model. This indicates higher uncertainty in the prediction of species richness, which is obtained by combining the prediction results of distribution change for each species, than in the prediction of distribution change. The turnover rate of the ant assemblages was predicted to increase with decreases in temperature and increases in elevation, which was consistent with other studies. Finally, our results showed that the prediction of the distribution or diversity of organisms responding to climate change is uncertain because of the high variability of the model outputs induced by the variables used in the models. [ABSTRACT FROM AUTHOR]

We investigated a screening method for mild cognitive impairment (MCI) that combined bioimpedance features and the Korean Mini-Mental State Examination (K-MMSE) score. Data were collected from 539 subjects aged 60 years or older at the Gwangju Alzheimer's & Related Dementias (GARD) Cohort Research Center, A total of 470 participants were used for the analysis, including 318 normal controls and 152 MCI participants. We measured bioimpedance, K-MMSE, and the Seoul Neuropsychological Screening Battery (SNSB-II). We developed a multiple linear regression model to predict MCI by combining bioimpedance variables and K-MMSE total score and compared the model's accuracy with SNSB-II domain scores by the area under the receiver operating characteristic curve (AUROC). We additionally compared the model performance with several machine learning models such as extreme gradient boosting, random forest, support vector machine, and elastic net. To test the model performances, the dataset was divided into a training set (70%) and a test set (30%). The AUROC values of SNSB-II scores were 0.803 in both sexes, 0.840 for males, and 0.770 for females. In the combined model, the AUROC values were 0.790 (0.773) for males (and females), which were significantly higher than those from the model including MMSE scores alone (0.723 for males and 0.622 for females) or bioimpedance variables alone (0.640 for males and 0.615 for females). Furthermore, the accuracies of the combined model were comparable to those of machine learning models. The bioimpedance-MMSE combined model effectively distinguished the MCI participants and suggests a technique for rapid and improved screening of the elderly population at risk of cognitive impairment. [ABSTRACT FROM AUTHOR]

Aim: As life expectancy increases worldwide, the prevalence and the disease burden of diabetes in older adults are also increasing. This study aimed to examine sex differences in the reciprocal relationship between glycemic control and depressive symptoms among older adults with diabetes by using longitudinal data at two timepoints. Methods: Wave 1 (W1, 2016–2017) and wave 2 (W2, 2018–2019) data from the Korean Frailty and Aging Cohort Study were used. Finally, 416 older adults with diabetes who satisfied the inclusion criteria were analyzed (215 males, 201 females). The reciprocal relationship between depressive symptoms and glycosylated hemoglobin A1c (HbA1c) levels was examined using a cross‐lagged panel model. Results: HbA1c levels and depressive symptoms at earlier time points were the most significant factors contributing to HbA1c levels and depressive symptoms at later timepoints. The relationship between HbA1c level and depressive symptoms differed according to sex. The cross‐path from depressive symptoms (W1) to HbA1c levels (W2) was positively statistically significant in males (β = 0.18, SD = 0.05, P = 0.001). The path from HbA1c levels (W1) to depressive symptoms (W2) was positively statistically significant in females (β = 0.12, SD = 0.06, P = 0.032). Conclusions: Early management of blood glucose levels and depressive symptoms is important in older adults with diabetes. Moreover, glycemic control through social activities in the community could be effective in relieving depressive symptoms in older females with diabetes, and managing depressive symptoms and glucose levels together could be effective in glycemic control in older males with diabetes. Geriatr Gerontol Int 2024; 24: 32–39. [ABSTRACT FROM AUTHOR]

Background: The objective of this study was to develop and validate machine learning models for data entry error detection in a national out-of-hospital cardiac arrest (OHCA) prehospital patient care report database. Methods: Adult OHCAs of presumed cardiac etiology were included. Data entry errors were defined as discrepancies between the coded data and the free-text note documenting the intervention or event; for example, information that was recorded as "absent" in the coded data but "present" in the free-text note. Machine learning models using the extreme gradient boosting, logistic regression, extreme gradient boosting outlier detection, and K-nearest neighbor outlier detection algorithms for error detection within nine core variables were developed and then validated for each variable. Results: Among 12,100 OHCAs, the proportion of cases with at least one error type was 16.2%. The area under the receiver operating characteristic curve (AUC) of the best-performing model (model with the highest AUC for each outcome variable) was 0.71–0.95. Machine learning models detected errors most efficiently for outcome place and initial rhythm errors; 82.6% of place errors and 93.8% of initial rhythm errors could be detected while checking 11 and 35% of data, respectively, compared to the strategy of checking all data. Conclusion: Machine learning models can detect data entry errors in care reports of emergency medical services (EMS) clinicians with acceptable performance and likely can improve the efficiency of the process of data quality control. EMS organizations that provide more prehospital interventions for OHCA patients could have higher error rates and may benefit from the adoption of error-detection models. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to identify the general characteristics and health behaviors of students with non-therapeutic substance use. Methods: This secondary data analysis used data from the 17th Korea Youth Risk Behavior Web-based Survey (2021). Analyses of the 54,848 adolescents used descriptive statisics, the Rao--Scott χ² test, and logistic regression. Results: The risk factors for substance use among students were anxiety, loneliness, living separately from family, suicidal ideation, e-cigarette use, and high stress. Conclusion: The findings help identify the risk factors for non-therapeutic drug use among adolescents. Since South Korea does not have a drug prevention program for its adolescent population, an educational plan based on these findings could help prevent adolescent substance abuse. [ABSTRACT FROM AUTHOR]

Background: The rising burden of thyroid cancer (TC) is a public health problem in Asia. Predicting the future burden of TC in Asian countries is essential for disease prevention. Methods: Data were obtained from the Global Burden of Disease 2019 for five Asian countries. We applied Bayesian age-period-cohort models to predict morbidity and mortality to 2035 and calculated age-standardized incidence rate (ASIR) and age-standardized mortality rate (ASMR). Furthermore, the estimated annual percentage change was calculated to evaluate the variation of ASIR and ASMR. Results: By 2035, predictions suggest that cases of TC will reach 75.56 x 10³ in China, 70.22 x 10³ in India, 15.78 x 103 in the Republic of Korea, 9.01 x 10³ in Japan and 5.55 x 10³ in Thailand, respectively. Except Japan, a significant upward trend of ASIR of TC will be observed in five Asian countries. The deaths from TC will increase in five countries and India will become the highest reaching 14.07 x 10³. The ASMR will rise to 0.83/100 000 in India and 1.06/100 000 in the Republic of Korea, while it will drop to 0.35/100 000 in China, 0.43/100 000 in Japan and 0.50/100 000 in Thailand. In further predictions projected by sex, the growth rate of ASIR is reported higher in males than in females among most countries. ASMR of male will exceed that of females in China and Thailand by 2035. Conclusion: The disease burden caused by TC will further increase in five Asian countries, especially for men. It is necessary to develop more rational and timely disease prevention and manage strategies facing this disease trend. [ABSTRACT FROM AUTHOR]

Background: Previous evidence on antidepressant medication and cardiovascular disease (CVD) among patients with posttraumatic stress disorder (PTSD) has been inconclusive. We estimated the association between antidepressant medication and CVD by applying a marginal structural model. Methods: We analyzed medical utilization records of 27 170 people with PTSD without prior major cardiovascular events in the Korean National Health Insurance Database (NHID). PTSD and CVD were defined in accordance with the recorded ICD-10 diagnostic codes. We acquired information on antidepressant use from the NHID and categorized them by medication type. A composite major adverse cardiovascular events (MACE) outcome was defined as coronary artery disease with revascularization, ischaemic stroke, and/or haemorrhagic stroke. We used inverse probability of treatment weighting to estimate the parameters of a marginal structural discrete-time survival analysis regression model, comparing the resulting estimates to those derived from traditional time-fixed and time-varying Cox proportional hazards regression. We calculated cumulative daily defined doses to test for a dose–response relationship. Results: People exposed to antidepressants showed a higher hazard of MACE [hazard ratio (HR) 1.34, 95% confidence interval (CI) 1.18–1.53]. The estimated effects were strongest for selective serotonin reuptake inhibitors (HR 1.24, 95% CI 1.08–1.44) and TCAs (HR 1.33, 95% CI 1.13–1.56). Exposure to serotonin-norepinephrine reuptake inhibitors did not appear to increase the risk of MACE. People exposed to higher doses of antidepressants showed higher risk of MACE. Conclusions: In a national cohort of people with PTSD, exposure to antidepressant medications increased the risk of MACE in a dose–response fashion. [ABSTRACT FROM AUTHOR]

The biotic community predictive model proposed in the River Invertebrate Prediction And Classification System which compares observed values at test sites with expected values in reference sites is used to perform biological assessments of rivers. In 2021, a Korean River Macroinvertebrate Prediction and Assessment System (KRIMPAS) for river integrity assessment was developed. However, since it was the early stage of development, improvement of the accuracy and precision of assessment was required. Therefore, benthic macroinvertebrates and six environmental variables collected from 357 reference sites between 2010 to 2021 were used to compare the prediction mothods and attempt to optimize the KRIMPAS. Based on the number of taxa (species and family levels) and six biological indices, the existing KRIMPAS method (Original method) was compared with two weighted average methods (W-Avg 1 and 2) according to the probability that a test site was assigned. In terms of statistical significance, observed to expected (O/E) values of the number of taxa, but, there were difference for most of biological indices. When the probability density function based on the log-normal distribution for each derivation method was applied, no difference was observed in the number of taxa; however, there were differences in the mean, median, mode, and variance for each biological index. A fit verification based on the normalized root-mean-square error (NRMSE) for each derivation method was conducted, and the overall fit tended to be higher with the W-Avg 1 and 2 when compared with the Original method. Thus, to correct the current biological assessments of Korean rivers we propose the application of the W-Avg method as this would improve the derivation of the predictive model. [ABSTRACT FROM AUTHOR]

The purpose of this study was to develop a prediction model for stroke-associated pneumonia (SAP) based on risk factors for SAP and to suggest nursing interventions to prevent SAP. In addition, a nomogram was developed to enhance its utility in nursing practice. The retrospective cohort study included 551 patients hospitalized for acute ischemic stroke at a university hospital in South Korea. Data were collected through a structured questionnaire and a review of the electronic medical record (EMR). In the development of a predictive model for SAP, multivariate logistic regression analysis showed that independent risk factors for SAP were age ≥ 65 years, National Institute of Health Stroke Scale (NIHSS) score ≥ 7, nasogastric tube feeding, and C-reactive protein (CRP) ≥ 5.0 mg/dL. The logit model was used to construct the SAP prediction nomogram, and the area under the curve (AUC) of the nomogram was 0.94. Furthermore, the slope of the calibration plot was close to the 45-degree line, indicating that the developed nomogram may be useful for predicting SAP. It is necessary to monitor the age, NIHSS score, nasogastric tube feeding status, and CRP level of stroke patients and identify high-risk groups using the developed nomogram to provide active nursing interventions to prevent SAP. [ABSTRACT FROM AUTHOR]

Background Few studies have comprehensively evaluated the risk of lung cancer in tuberculosis survivors with consideration of smoking status and chronic obstructive pulmonary disease (COPD). Furthermore, little is known about lung cancer risk factors in tuberculosis survivors. Methods This population-based cohort study enrolled tuberculosis survivors (n = 75 467) between 2010 and 2017 and 1:1 age- and sex-matched controls. Subjects were followed up for 1 year from the date of tuberculosis diagnosis to the date of the incident lung cancer, death, or December 2018, whichever came first. The risk of lung cancer was evaluated according to smoking and COPD status. We also evaluated the risk factors for lung cancer and developed an individualized lung cancer prediction model for tuberculosis survivors. Results During a median follow-up duration of 4.8 years, the incident lung cancer risk was 1.72-fold higher in tuberculosis survivors than in the controls. Among tuberculosis survivors, those who were current smokers with ≥20 pack-years showed the highest risk of lung cancer (adjusted hazard ratio, 6.78) compared with never-smoker, non–tuberculosis-infected controls. tuberculosis survivors with COPD had a higher risk (2.43) than non-COPD, non–tuberculosis-infected controls. Risk factors for lung cancer in tuberculosis survivors were pulmonary tuberculosis, age >60 years, smoking, and the presence of COPD or asthma. The individualized lung cancer risk model showed good discrimination (concordance statistic = 0.827). Conclusions Previous tuberculosis infection is an independent risk factor regardless of smoking status or amount and COPD. Closer monitoring of tuberculosis survivors, especially heavy smokers or those with COPD, is needed for early lung cancer diagnosis. [ABSTRACT FROM AUTHOR]

Purpose: Continuous Glucose Monitoring (CGM) plays a crucial role in helping patients with diabetes manage their blood sugar levels. This review aimed to understand the context and scope of CGM-related studies in Korea and identified areas for future research, particularly in nursing science. Methods: The search strategy involved examining eight electronic databases for published studies on CGM, with the search period ending in March 2022. Four independent reviewers conducted screening, selection, and narrative review of the studies. Results: Descriptive and substantive analyses were performed for 68 studies on CGM, which covered diverse areas, including: (1) evaluating the CGM performance through comparisons with self-monitoring of blood glucose and correlations with existing indices; (2) validating the efficacy of CGM in improving patient outcomes and assessing various interventions; and (3) expanding the use of CGM, such as clinical guidelines and reviews, developing predictive models, and other clinical studies. Conclusion: Research on CGM has expanded to include early performance evaluation, efficacy verification, and utilization in various clinical settings. However, there is a lack of nursing-focused studies of CGM. It is recommended to incorporate CGM in nursing research aimed at enhancing self-management for patients with diabetes. Future projects should focus on developing and applying patient-customized CGM user manuals, comparing the effectiveness of CGM among patients with different types of diabetes, exploring qualitative experiences of CGM use, and addressing related issues. [ABSTRACT FROM AUTHOR]

Since the WHO's 2021 aging redefinition emphasizes "healthy aging" by focusing on the elderly's ability to perform daily activities, sarcopenia, which is defined as the loss of skeletal muscle mass, is now becoming a critical health concern, especially in South Korea with a rapidly aging population. Therefore, we develop a prediction model for sarcopenia by using machine learning (ML) techniques based on the Korea National Health and Nutrition Examination Survey (KNHANES) data 2008–2011, in which we focus on the role of socioeconomic status (SES), social infrastructure, and quality of life (QoL) in the prevalence of sarcopenia. We successfully identify sarcopenia with approximately 80% accuracy by using random forest (RF) and LightGBM (LGB), CatBoost (CAT), and a deep neural network (DNN). For prediction reliability, we achieve area under curve (AUC) values of 0.831, 0.868, and 0.773 for both genders, males, and females, respectively. Especially when using only male data, all the models consistently exhibit better performance overall. Furthermore, using the SHapley Additive exPlanations (SHAP) analysis, we find several common key features, which mainly contribute to model building. These include SES features, such as monthly household income, housing type, marriage status, and social infrastructure accessibility. Furthermore, the causal relationships of household income, per capita neighborhood sports facility area, and life satisfaction are analyzed to establish an effective prediction model for sarcopenia management in an aging population. [ABSTRACT FROM AUTHOR]

Chest injuries that occur in motor vehicle crashes (MVCs) include rib fractures, pneumothorax, hemothorax, and hemothorax depending on the injury mechanism. Many risk factors are associated with serious chest injuries from MVCs. The Korean In-Depth Accident Study database was analyzed to identify risk factors associated with motor vehicle occupants' serious chest injury. Among 3,697 patients who visited the emergency room in regional emergency medical centers after MVCs between 2011 and 2018, we analyzed data from 1,226 patients with chest injuries. Vehicle damage was assessed using the Collision Deformation Classification (CDC) code and images of the damaged vehicle, and trauma scores were used to determine injury severity. Serious chest injury was defined as an Abbreviated Injury Scale (AIS) score for the chest code was more than 3. The patients were divided into two groups: serious chest injury patients with MAIS ≥ 3 and those with non-serious chest injury with MAIS < 3. A predictive model to analyze the factors affecting the presence of serious chest injury in the occupants on MVCs was constructed by a logistic regression analysis. Among the 1,226 patients with chest injuries, 484 (39.5%) had serious chest injuries. Patients in the serious group were older than those in the non-serious group (p=.001). In analyses based on vehicle type, the proportion of light truck occupants was higher in the serious group than in the non-serious group (p=.026). The rate of seatbelt use was lower in the serious group than in the non-serious group (p=.008). The median crush extent (seventh column of the CDC code) was higher in the serious group than in the non-serious group (p<.001). Emergency room data showed that the rates of intensive care unit (ICU) admission and death were higher among patients with serious injuries (p<.001). Similarly, the general ward/ICU admission data showed that the transfer and death rates were higher in patients with serious injuries (p<.001). The median ISS was higher in the serious group than in the non-serious group (p<.001). A predictive model was derived based on sex, age, vehicle type, seating row, belt status, collision type, and crush extent. This predictive model had an explanatory power of 67.2% for serious chest injuries. The model was estimated for external validation using the confusion matrix by applying the predictive model to the 2019 and 2020 data of the same structure as the data at the time of model development in the KIDAS database. Although this study had a major limitation in that the explanatory power of the predictive model was weak due to the small number of samples and many exclusion conditions, it was meaningful in that it suggested a model that could predict serious chest injuries in motor vehicle occupants (MVOs) based on actual accident investigation data in Korea. Future studies should yield more meaningful results, for example, if the chest compression depth value is derived through the reconstruction of MVCs using accurate collision speed values, and better models can be developed to predict the relationship between these values and the occurrence of serious chest injury. [ABSTRACT FROM AUTHOR]

Background: It is challenging to predict retear after arthroscopic rotator cuff repair (ARCR). The usefulness of arthroscopic intraoperative images as predictors of the ARCR prognosis has not been analyzed. Purpose: To evaluate the usefulness of arthroscopic images for the prediction of retear after ARCR using deep learning (DL) algorithms. Study Design: Cohort study (Diagnosis); Level of evidence, 2. Methods: In total, 1394 arthroscopic intraoperative images were retrospectively obtained from 580 patients. Repaired tendon integrity was evaluated using magnetic resonance imaging performed within 2 years after surgery. Images obtained immediately after ARCR were included. We used 3 DL architectures to predict retear based on arthroscopic images. Three pretrained DL algorithms (VGG16, DenseNet, and Xception) were used for transfer learning. Training and test sets were split into 8:2. Threefold stratified validation was used to fine-tune the hyperparameters using the training data set. The validation results of each fold were evaluated. The performance of each model in the test set was evaluated in terms of accuracy, area under the receiver operating characteristic curve (AUC), F1-score, sensitivity, and specificity. Results: In total, 1138 and 256 arthroscopic images were obtained from 514 patients and 66 patients in the nonretear and retear groups, respectively. The mean validation accuracy of each model was 83% for VGG16, 89% for Xception, and 91% for DenseNet. The accuracy for the test set was 76% for VGG16, 87% for Xception, and 91% for DenseNet. The AUC was highest for DenseNet (0.92); it was 0.83 for VGG16 and 0.91 for Xception. For the test set, the specificity and sensitivity were 0.93 and 0.84 for DenseNet, 0.89 and 0.84 for Xception, and 0.70 and 0.80 for VGG16, respectively. Conclusion: The application of DL algorithms to intraoperative arthroscopic images has demonstrated a high level of accuracy in predicting retear occurrences. [ABSTRACT FROM AUTHOR]

Water distribution systems consistently supply high-quality water at suitable pressure and volume for human and industrial consumption. Meticulous water quality management is vital to these systems. South Korea, having established legal standards for water distribution in 1963, operates the National Auto Water Quality Monitoring System for real-time water quality monitoring and contamination warnings when levels exceed legal thresholds. The U.S. Environmental Protection Agency (EPA) points out that fixed thresholds can trigger an abundance of false-positive alarms, causing irregular hydraulic changes, and false-negative errors. This could potentially lead to a failure in detecting initial instances of pollution or micropollution that fall below the established threshold. To address this, our study developed an proactive contamination warning method for South Korea's monitoring system, utilizing long short-term memory (LSTM) for water quality prediction. We also employed ensemble empirical mode decomposition (EEMD) in feature engineering to enhance LSTM's prediction performance. Additionally, we devised an optimal water quality prediction model development methodology by comparing short- and long-term prediction performances. Our findings revealed that using EEMD for feature engineering improved the stability and reduced the prediction lag of LSTM, outperforming traditional methods. This refined approach offers a more reliable and efficient means of monitoring and managing water quality in distribution systems. [ABSTRACT FROM AUTHOR]

Simple Summary: Ungulate–vehicle collisions (UVC) often threaten human life and property due to the large body size of ungulates. The purpose of this study is to understand factors contributing to UVC of three ungulate species (Capreolus pygargus, Hydropotes inermis, and Sus scrofa) in the Republic of Korea using predictive modeling. We relied on the UVC data of the Korea Roadkill Observation System, a government-sponsored web-based system that is required for road workers to report roadkill incidents on all major road types across the country with a standardized data collection method. There were 25,755 UVC datapoints between 2019 and 2021. The UVC frequencies tended to be the highest in the most active seasons of the year, such as dispersal or mating seasons. Factors critical for UVC frequencies were different among three ungulate species, suggesting individualized mitigation plans. Animal–vehicle collisions (AVC) threaten animals as well as human life and property. AVC with ungulates, called ungulate–vehicle collision (UVC), often seriously endangers human safety because of the considerable body size of ungulates. In the Republic of Korea, three ungulate species, Capreolus pygargus, Hydropotes inermis, and Sus scrofa, account for a large proportion of AVC. This study aimed to understand the characteristics of UVC by examining various parameters related to habitat, traffic, and seasonality using MaxEnt. The results showed that the peak UVC seasons coincided with the most active seasonal behaviors of the studied ungulates. For the modeling results, in C. pygargus, habitat variables are most important for models across seasons, and UVC events are most likely to occur in high mountain chains. In H. inermis, habitat and traffic variables are most important for models across seasons. Although the important habitat for the models were different across seasons for S. scrofa, the maximum speed was consistently critical for models across all seasons. Factors critical to UVC in the Republic of Korea were different for the three ungulate species and across seasons, indicating that seasonal behavior should be considered along with landscape and traffic characteristics to mitigate UVC. [ABSTRACT FROM AUTHOR]

Summary: The need for an accurate country-specific real-world-based fracture prediction model is increasing. Thus, we developed scoring systems for osteoporotic fractures from hospital-based cohorts and validated them in an independent cohort in Korea. The model includes history of fracture, age, lumbar spine and total hip T-score, and cardiovascular disease. Purpose: Osteoporotic fractures are substantial health and economic burden. Therefore, the need for an accurate real-world-based fracture prediction model is increasing. We aimed to develop and validate an accurate and user-friendly model to predict major osteoporotic and hip fractures using a common data model database. Methods: The study included 20,107 and 13,353 participants aged ≥ 50 years with data on bone mineral density using dual-energy X-ray absorptiometry from the CDM database between 2008 and 2011 from the discovery and validation cohort, respectively. The main outcomes were major osteoporotic and hip fracture events. DeepHit and Cox proportional hazard models were used to identify predictors of fractures and to build scoring systems, respectively. Results: The mean age was 64.5 years, and 84.3% were women. During a mean of 7.6 years of follow-up, 1990 major osteoporotic and 309 hip fracture events were observed. In the final scoring model, history of fracture, age, lumbar spine T-score, total hip T-score, and cardiovascular disease were selected as predictors for major osteoporotic fractures. For hip fractures, history of fracture, age, total hip T-score, cerebrovascular disease, and diabetes mellitus were selected. Harrell's C-index for osteoporotic and hip fractures were 0.789 and 0.860 in the discovery cohort and 0.762 and 0.773 in the validation cohort, respectively. The estimated 10-year risks of major osteoporotic and hip fractures were 2.0%, 0.2% at score 0 and 68.8%, 18.8% at their maximum scores, respectively. Conclusion: We developed scoring systems for osteoporotic fractures from hospital-based cohorts and validated them in an independent cohort. These simple scoring models may help predict fracture risks in real-world practice. [ABSTRACT FROM AUTHOR]

Korea is showing the fastest trend in the world in population aging; there is a high interest in the elderly population nationwide. Among the common chronic diseases, the elderly tends to have a high incidence of depression. That said, it has been vital to focus on preventing depression in the elderly in advance. Hence, this study aims to select the factors related to depression in low-income seniors identified in previous studies and to develop a prediction model. In this study, 2975 elderly people from low-income families were extracted using the 13th-year data of the Korea Welfare Panel Study (2018). Decision trees, logistic regression, neural networks, and random forest were applied to develop a predictive model among the numerous data mining techniques. In addition, the wrapper's stepwise backward elimination, which finds the optimal model by removing the least relevant factors, was applied. The evaluation of the model was confirmed via accuracy. It was verified that the final prediction model, in the case of a decision tree, showed the highest predictive power with an accuracy of 97.3%. Second, psychological factors, leisure life satisfaction, social support, subjective health awareness, and family support ranked higher than demographic factors influencing depression. Based on the results, an approach focused on psychological support is much needed to manage depression in low-income seniors. As predicting depression in the elderly varies on numerous influencing factors, using a decision tree may be beneficial to establish a firm prediction model to identify vital factors causing depression in the elderly population. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing trend of obesity and dyslipidemia among young adult men in South Korea. Therefore, we aimed to identify the obesity-related factors of dyslipidemia among young adult men in Korea using 3-year data (2019–2021) from the 8th Korea National Health and Nutrition Examination Survey. We included 1559 eligible men aged 19–39 years and examined the association between dyslipidemia and participants' general characteristics, health-related characteristics, and food intake according to obesity status. Statistical analysis was performed using complex sample analysis with weighted household and individual data. The affecting factors of dyslipidemia included age, body image perception, stress, and waist circumference (WC) in the body mass index (BMI) < 25 kg/m2 group and age and WC in the BMI ≥ 25 kg/m2 group. To prevent and manage dyslipidemia in young adult men, interventions should be developed separately for the normal, underweight, and obese groups. Effective intervention requires measuring WC and focusing on body fat control. Moreover, regular screening of this population should be performed to ensure early diagnosis and management of dyslipidemia. [ABSTRACT FROM AUTHOR]

Background: Although many studies have been conducted to examine predictors of quality of life (QoL), little information exists on the real-world application of Rector's conceptual model for QoL related to heart failure (HF). Objectives: In this study, we aimed to examine a hypothetical model of QoL based on Rector's conceptual model for QoL in relation to HF and the existing literature on patients with HF. Methods: Using a cross-sectional survey, 165 patients with HF were recruited from an outpatient clinic in Korea. Data were collected based on Rector's model constructs, such as cardiac function, symptoms, functional limitation, depression, distress, and QoL. Left ventricular ejection fraction for cardiac function was measured using echocardiography. Results: Functional limitation, depression, and distress, but not symptoms, had a direct effect on QoL (all P s <.001). Cardiac function and symptoms directly affected functional limitation (β = 0.186, P =.004, and β = −0.488, P = <.001, respectively), whereas cardiac function, symptoms, and depression affected QoL through functional limitation and distress. Conclusions: These results confirm that the Rector's model is suitable for predicting QoL in patients with HF. These findings have potential to guide and inform intervention programs designed to alleviate symptoms in patients with HF, enhance their physical functioning, and moderate their psychological distress with the ultimate goal of improving their QoL. [ABSTRACT FROM AUTHOR]

Facial expression recognition technology has been utilized both for entertainment purposes and as a valuable aid in rehabilitation and facial exercise assistance. This technology leverages artificial intelligence models to predict facial landmark points and provide visual feedback, thereby facilitating users' facial movements. However, feedback designs that disregard user preferences may cause discomfort and diminish the benefits of exercise. This study aimed to develop a feedback design guide for facial rehabilitation exercises by investigating user responses to various feedback design methods. We created a facial recognition mobile application and designed six feedback variations based on shape and transparency. To evaluate user experience, we conducted a usability test involving 48 participants (24 subjects in their 20s and 24 over 60 years of age), assessing factors such as feedback, assistance, disturbance, aesthetics, cognitive ease, and appropriateness. The experimental results revealed significant differences in transparency, age, and the interaction between transparency and age. Consequently, it is essential to consider both transparency and user age when designing facial recognition feedback. The findings of this study could potentially inform the design of more effective and personalized visual feedback for facial motion, ultimately benefiting users in rehabilitation and exercise contexts. [ABSTRACT FROM AUTHOR]

Background: Little is known about accuracy and confidence of clinicians' prediction of survival (CPS) in East-Asian countries. Objective: We aimed to examine accuracy of CPS for 7-, 21-, and 42-day survival in palliative inpatients and its association with prognostic confidence. Design: An international prospective cohort study in Japan (JP), Korea (KR), and Taiwan (TW). Setting/Subjects: Subjects were inpatients with advanced cancer admitted to 37 palliative care units in three countries. Measurements: Discrimination of CPS was investigated through sensitivity, specificity, overall accuracy, and area under the receiver operating characteristics curves (AUROCs) according to 7-, 21-, and 42-day survival. The accuracies of CPS were compared with those of Performance Status-based Palliative Prognostic Index (PS-PPI). Clinicians were instructed to rate confidence level on a 0–10-point scale. Results: A total of 2571 patients were analyzed. The specificity was highest at 93.2–100.0% for the 7-day CPS, and sensitivity was highest at 71.5–86.8% for the 42-day CPS. The AUROCs of the seven-day CPS were 0.88, 0.94, and 0.89, while those of PS-PPI were 0.77, 0.69, and 0.69 for JP, KR, and TW, respectively. As for 42-day prediction, sensitivities of PS-PPI were higher than those of CPS. Clinicians' confidence was strongly associated with the accuracy of prediction in all three countries (all p-values <0.01). Conclusions: CPS accuracies were highest (0.88–0.94) for the seven-day survival prediction. CPS was more accurate than PS-PPI in all timeframe prediction except 42-day prediction in KR. Prognostic confidence was significantly associated with the accuracy of CPS. [ABSTRACT FROM AUTHOR]

Nurse turnover is a critical issue in Korea, as it affects the quality of patient care and increases the financial burden on healthcare systems. To address this problem, this study aimed to develop and evaluate a machine learning-based prediction model for nurse turnover in Korea and analyze factors influencing nurse turnover. The study was conducted in two phases: building the prediction model and evaluating its performance. Three models, namely, decision tree, logistic regression, and random forest were evaluated and compared to build the nurse turnover prediction model. The importance of turnover decision factors was also analyzed. The random forest model showed the highest accuracy of 0.97. The accuracy of turnover prediction within one year was improved to 98.9% with the optimized random forest. Salary was the most important decision factor for nurse turnover. The nurse turnover prediction model developed in this study can efficiently predict nurse turnover in Korea with minimal personnel and cost through machine learning. The model can effectively manage nurse turnover in a cost-effective manner if utilized in hospitals or nursing units. [ABSTRACT FROM AUTHOR]

Developing a machine-learning-based radiative transfer emulator in a weather forecasting model is valuable because it can significantly improve the computational speed of forecasting severe weather events. To replace the radiative transfer parameterization in the weather forecasting model, the universal applicability of the radiation emulator is essential, indicating a transition from the research to the operational level. This study investigates the degradation of the forecast accuracy of the radiation emulator for the Korea peninsula when it is tested at different horizontal resolutions (100–0.25 km) concerning the accuracy attained at the training resolution (5 km) for universal applications. In real-case simulations (100–5 km), the forecast errors of radiative fluxes and precipitation were reduced at coarse resolutions. Ideal-case simulations (5–0.25 km) showed larger errors in heating rates and fluxes at fine resolutions, implying the difficulty in predicting heating rates and fluxes at cloud-resolving scales. However, all simulations maintained an appropriate accuracy range compared with observations in real-case simulations or the infrequent use of radiative transfer parameterization in ideal-case simulations. These findings demonstrate the feasibility of a universal radiation emulator associated with different resolutions/models and emphasize the importance of emulating high-resolution modeling in the future. [ABSTRACT FROM AUTHOR]

Clays in fault zones have low electrical resistivity, making electrical resistivity tomography (ERT) effective for fault investigations. However, traditional ERT inversion methods struggle to find a unique solution and produce unstable results owing to the ill-posed nature of the problem. To address this, a workflow integrating deep-learning (DL) technology with traditional ERT inversion is proposed. First, a deep-learning model named DL-ERT inversion that maps apparent resistivity data to subsurface resistivity models is developed. To create target-oriented training data, we use approximately 150 field borehole data acquired from various survey areas in South Korea. The DL-ERT inversion algorithm is based on a U-Net structure and includes an additional network called the borehole mixer to incorporate borehole information when available. The DL-ERT inversion model is trained in three stages: base model training, borehole mixer training, and fine-tuning. Results showed that the fine-tuning model provided the highest prediction accuracy for all test datasets. Next, the prediction of the trained model is used as the initial model for the deterministic inversion method to predict the final subsurface model. The efficiency and accuracy of the proposed workflow are demonstrated in fault detection using a field data example compared with traditional deterministic inversion. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to verify the potential use of postoperative pulmonary complications prediction models in patients with lung resection. Methods: In this retrospective study, 1,160 patients were selected among the admitted patients who underwent lung resection surgery. The predictive validity of the Assess Respiratory Risk in Surgical Patients in Catalonia Tool (ARISCAT) and the Pulmonary Complications Risk Score (PCRS)-lung resection model were assessed based on the sensitivity, specificity, positive and negative predictive values, and Area Under the receiver operating characteristic Curve (AUC). Results: Of the patients, 420 (36.2%) developed postoperative pulmonary complications after lung resection surgery within 30 days. The sensitivity, specificity, positive predictive values, and negative predictive values were 52.2%, 70.6%, 49.0%, and 74.0%, respectively, for the ARISCAT (cut-off point of 47), and 53.8%, 78.5%, 15.3%, and 95.9%, respectively, for the PCRS-lung resection model (cut-off point of 147). The AUCs were 0.65 (ARISCAT) and 0.70 (PCRS-lung resection model). Conclusion: The findings indicate that the predictive validity values of the ARISCAT was sufficient, and the PCRS-lung resection model was good. However, the clinical usefulness of the models should be verified in future studies. [ABSTRACT FROM AUTHOR]

Sarcopenia is a well-known age-related disease that can lead to musculoskeletal disorders and chronic metabolic syndromes, such as sarcopenic obesity. Numerous studies have researched the relationship between sarcopenia and various risk factors, leading to the development of predictive models based on these factors. In this study, we explored the impact of physical activity (PA) in daily life and obesity on sarcopenia prediction. PA is easier to measure using personal devices, such as smartphones and watches, or lifelogs, than using other factors that require medical equipment and examination. To demonstrate the feasibility of sarcopenia prediction using PA, we trained various machine learning models, including gradient boosting machine (GBM), xgboost (XGB), lightgbm (LGB), catboost (CAT), logistic regression, support vector classifier, k-nearest neighbors, random forest (RF), multi-layer perceptron, and deep neural network (DNN), using data samples from the Korea National Health and Nutrition Examination Survey. Among the models, the DNN achieved the most precise accuracy on average, 81%, with PA features across all data combinations, and the accuracy increased up to 90% with the addition of obesity information, such as total fat mass and fat percentage. Considering the difficulty of measuring the obesity feature, when adding waist circumference to the PA features, the DNN recorded the highest accuracy of 84%. This model accuracy could be improved by using separate training sets according to gender. As a result of measurement with various metrics for accurate evaluation of models, GBM, XGB, LGB, CAT, RF, and DNN demonstrated significant predictive performance using only PA features including waist circumference, with AUC values at least around 0.85 and often approaching or exceeding 0.9. We also found the key features for a highly performing model such as the quantified PA value and metabolic equivalent score in addition to a simple obesity measure such as body mass index (BMI) and waist circumference using SHAP analysis. [ABSTRACT FROM AUTHOR]

As ground subsidence accidents in urban areas that occur due to damage to underground utilities can cause great damage, it is necessary to predict and prepare for such accidents in order to minimize such damage. It has been reported that the main cause of ground subsidence in urban areas is cavities in the ground formed by damage to underground utilities. Thus, in this study, attribute information and historical ground subsidence information of six types of underground utility lines (water supply, sewage, power, gas, heating, and communication) were collected to develop a ground subsidence risk prediction model based on machine learning. To predict the risk of ground subsidence in the target area, it was divided into a grid with a square size of 500 m × 500 m, and attribute information of underground utility lines and historical information of ground subsidence included in the grid were extracted. Six types of underground utility lines were merged into single-type attribute information, and the risk of ground subsidence was categorized into three levels using the number of ground subsidence occurrences to develop a dataset. In addition, 12 datasets, which were developed based on the conditions of certain divided ranges of attribute information and risk levels, and 12 additional datasets, which were developed using the Synthetic Minority Oversampling Technique to resolve the imbalance of data, were built. Then, factors that represented significant correlations between input and output data were singled out and were then applied to the RandomForest, XGBoost, and LightGBM algorithms to select a model that produced the best performance. By classifying the ground subsidence risk levels through the selected model, it was found that density was the most important influencing factor used in the model. A risk map of ground subsidence in the target area was made through the model; the map showed the trend of well-predicted risk levels in the area where ground subsidence was concentrated. [ABSTRACT FROM AUTHOR]