• Psychiatry investigation
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• v.15 no.11
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• pp.1030-1036
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• 2018

Objective In this study, we aimed to develop a model predicting individuals with suicide ideation within a general population using a machine learning algorithm. Methods Among 35,116 individuals aged over 19 years from the Korea National Health & Nutrition Examination Survey, we selected 11,628 individuals via random down-sampling. This included 5,814 suicide ideators and the same number of non-suicide ideators. We randomly assigned the subjects to a training set (n=10,466) and a test set (n=1,162). In the training set, a random forest model was trained with 15 features selected with recursive feature elimination via 10-fold cross validation. Subsequently, the fitted model was used to predict suicide ideators in the test set and among the total of 35,116 subjects. All analyses were conducted in R. Results The prediction model achieved a good performance [area under receiver operating characteristic curve (AUC)=0.85] in the test set and predicted suicide ideators among the total samples with an accuracy of 0.821, sensitivity of 0.836, and specificity of 0.807. Conclusion This study shows the possibility that a machine learning approach can enable screening for suicide risk in the general population. Further work is warranted to increase the accuracy of prediction.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.1
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• pp.17-21
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• 2023

The primary objective of this research is to utilize machine learning models to analyze factors tailored to each dataset for predicting mental health conditions. The study aims to develop appropriate models based on specific datasets, with the goal of accurately predicting mental health states through the analysis of distinct factors present in each dataset. This approach seeks to design more effective strategies for the prevention and intervention of depression, enhancing the quality of mental health services by providing personalized services tailored to individual circumstances. Overall, the research endeavors to advance the development of personalized mental health prediction models through data-driven factor analysis, contributing to the improvement of mental health services on an individualized basis.

• Health Communication
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• v.13 no.2
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• pp.141-148
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• 2018

Background: The purpose of this study is to identify the degree of emotional labor, mental health care, care performance of certified caregivers for elder with dementia, and the factors that affect care performance. Methods: In order to collect data, structured questionnaire was used for 197 caregivers who worked at 3 dementia specialized facilities located in D city. Data were analyzed by t-test, ANOVA, correlation and multiple regression using SPSS/WIN 20.0. Results: Care performance had negative relationship with emotional labor(r=-.320, p<.000) and mental health(r=-.240, p<=001). Emotional labor had positive relationship with mental health(r=.208, p=.003) And the prediction factors influencing care performance were health status(${\beta}=.363$, p<.001), emotional labor(${\beta}=-.242$, p<.001), mental health(${\beta}=-.223$, p=.001). The total variance was 38.9% by predictors(F=25.978, p<.001). Conclusion: Based on the results of this study, in order to improve the care performance mental health program should be provided and good health management is needed to improve health status. And also it is necessary to develop and apply new strategies to reduce emotional labor of the dementia facility caregivers.

• Journal of the Korean Society of School Health
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• v.36 no.3
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• pp.113-125
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• 2023

Purpose: The purpose of this study is to develop a model for predicting mental health risk among adolescents based on health behavior information by employing a hybrid machine learning method. Methods: The study analyzed data of 51,850 domestic middle and high school students from 2022 Youth Health Behavior Survey conducted by the Korea Disease Control and Prevention Agency. Firstly, mental health risk levels (stress perception, suicidal thoughts, suicide attempts, suicide plans, experiences of sadness and despair, loneliness, and generalized anxiety disorder) were classified using the k-mean unsupervised learning technique. Secondly, demographic factors (family economic status, gender, age), academic performance, physical health (body mass index, moderate-intensity exercise, subjective health perception, oral health perception), daily life habits (sleep time, wake-up time, smartphone use time, difficulty recovering from fatigue), eating habits (consumption of high-caffeine drinks, sweet drinks, late-night snacks), violence victimization, and deviance (drinking, smoking experience) data were input to develop a random forest model predicting mental health risk, using logistic and XGBoosting. The model and its prediction performance were compared. Results: First, the subjects were classified into two mental health groups using k-mean unsupervised learning, with the high mental health risk group constituting 26.45% of the total sample (13,712 adolescents). This mental health risk group included most of the adolescents who had made suicide plans (95.1%) or attempted suicide (96.7%). Second, the predictive performance of the random forest model for classifying mental health risk groups significantly outperformed that of the reference model (AUC=.94). Predictors of high importance were 'difficulty recovering from daytime fatigue' and 'subjective health perception'. Conclusion: Based on an understanding of adolescent health behavior information, it is possible to predict the mental health risk levels of adolescents and make interventions in advance.

• The Journal of Bigdata
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• v.7 no.1
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• pp.29-36
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• 2022

The prevalence of stress and depression among emotional workers is increasing due to the rapid increase in emotional labor and service workers. However, the current mental health management of emotional workers is difficult to consider the emotional response at the time of stress situations, and the existing mental health management is limited because the individual's base state is not reflected. In this study, we present mental healthcare digital twin solution technology, a personalized stress risk management solution. For mental health risk management due to emotional labor, a solution simulation is performed to accurately predict stress risk through synchronization/modeling of dynamic objects in virtual space by extracting individual stress risk factors such as emotional/physical response and environment into various modalities. It provides a mental healthcare digital twin solution for predicting personalized mental health risks that can be configured with modalities and objects tailored to the environment of emotional workers and improved according to user feedback.

• Journal of Korean Academy of Nursing
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• v.35 no.2
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• pp.239-251
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• 2005

Purpose: This study was designed to construct a structural model for explaining mental health status in middle - aged women. Methods: The data was collected by self - reported questionnaires from 206 middle - aged women in Seoul. Data analysis was done with the SAS pc program for descriptive statistics and a PC - LISREL Program for finding the best fit model which assumes causal relationships among variables. Results: The overall fit of the hypothetical model to the data was good, but paths and variables of the model were modified by considering theoretical implications and statistical significances of parameter estimates. Thus it was modified by excluding 3 paths, The modified model showed was good fit to the data($x^2=177.55$, p=.00), GFI=0.908, AGFI=0.860, RMR=0.013, NFI=0.972, NNFI=0.982). Perceived stress, anger expression method, and self -esteem were found to have direct effects on mental health status in middle - aged women. These predictive variables of mental health status explained $66.6\%$ of the model. Conclusion: Programs to enhance mental health status in middle - aged women should include stress management skill, anger expression skill, and self -esteem enhancement skills to be effective.

• Journal of Digital Convergence
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• v.15 no.2
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• pp.251-262
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• 2017

This study distinguishes between factors that affect mental health of college students, establishes an effective approach to integrating model building, mental health promotion, and development of nursing intervention based on the Bronfenbrenner's ecological system theory. The study method investigate the causal relationship between the factors. The SPSS 20.0 program was used for general characteristics and mental health related characteristics. The fitness of the model was verified and the Amos 20.0 program was used for hypothesis verification. In the study, the fit index of the model was $x^2=614.90$ (p = .000), Q value = 3.5, GFI = .88, AGFI = .84, NFI = .92, NNFI = .94, CFI = .02, and RMSEA = .08, respectively. The results showed that stress was the most influential on mental health, and that stress coping strategies, self - esteem and parenting attitude affect mental health. In order to improve the mental health of college students, intervention should be carried out to develop nursing interventions to improve stress management, self - esteem, and coping with stress.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.9
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• pp.243-250
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• 2018

This descriptive study was conducted to investigate the factors affecting the mental health of university students. Data were collected from 312 university students by questionnaires and analyzed by t-test, ANOVA, Scheffe's test, correlation analysis, and multiple regression analysis. The mean scores were 1.69, 1.87 and 2.21 out of 5 on Likert scales for mental health, campus life stress and employment stress, respectively. The mean score for self-esteem was 2.27 out of 4 on a Likert scale. Gender and number of close friends affected mental health significantly. Moreover, there was a negative correlation between mental health and self-esteem(r=-.426, p<0.001), while a positive correlation was observed between mental health and campus life stress (r=0.660, p<0.001), and mental health and employment stress(r=.517, p<0.001). Multiple regression analysis showed that campus life stress (${\beta}=.545$), self-esteem(${\beta}=-.145$), and employment stress (${\beta}=0.067$) affected mental health in order, and the three research variables led to a 45.2% prediction for mental health of university students. Based on the results of this study, effective systematic plans for decreasing campus life stress and employment stress and increasing self-esteem are needed to improve the mental health of university students.

• Journal of Korean Academy of Nursing Administration
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• v.16 no.3
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• pp.229-239
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• 2010

Purpose: This study aimed to investigate the impact of nursing professionalism on the nursing performance and retention intention among psychiatric mental health nurses. Methods: As a descriptive correlational study, this study sampled 206 psychiatric mental health nurses in six hospitals in Seoul and Gyeonggi area through convenience sampling. Data were collected from March 2 to 31, 2009 using a self-report questionnaire. The collected data were analyzed using SPSS WIN 16.0. Results: In the subscales of professionalism, the 'Sense of calling' had the highest mean score while the 'Professional organization' had the lowest mean score. A significant positive correlation was observed in nursing professionalism, nursing performance and retention intention. According to an analysis on the impact of each subscale of nursing professionalism on nursing performance and retention intention, the 'Sense of calling' and 'Autonomy' were the most significant predictor variable. Conclusion: The results confirmed that the improvement of psychiatric mental health nurses' professionalism increases their nursing performance and retention intention and the 'Sense of calling' and 'Autonomy' are critical prediction factors. It is necessary to come up with a strategy which strengthens nursing professionalism in order to improve psychiatric mental health nurses' performance and retention intention.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.774-776
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• 2007

Because many people suffer physical and mental damage from the noise of the traffic facilities including road, rail, airport, the advanced countries have conducted the researches of predicting and solving the impact of the human health exposed to traffic noise. Therefore, this study suggests the fundamental plans which can assess the health impact of traffic noise on the basis of the prediction results about the health impact of traffic noise.