• Psychiatry investigation
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• 제15권11호
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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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• 제1권1호
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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.

• 의료커뮤니케이션
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• 제13권2호
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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.

• 한국학교보건학회지
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• 제36권3호
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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.

• 한국빅데이터학회지
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• 제7권1호
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• pp.29-36
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• 2022

감정 노동 및 서비스업 종사자의 급격한 증가에 따른 감정노동자의 스트레스 및 우울증 유병률이 증가하고 있다. 하지만, 현재 감정노동자의 정신건강 관리는 스트레스 상황 당시의 정서반응을 고려하기 어렵고 개인의 기저 상태가 반영되지 않아 기존 정신건강 관리의 한계가 존재한다. 본 연구에서는 개인 맞춤형 스트레스 위험 관리 솔루션인 멘탈 헬스케어 디지털 트윈 솔루션 기술을 제시한다. 감정노동으로 인한 정신건강 위험 관리를 위해, 정서/신체반응 및 환경 등의 개인별 스트레스 위험요인을 다양한 모달리티로 추출하고 가상 공간에서 동적 객체의 동기화/모델링을 통하여 스트레스 위험도를 정밀 예측하는 솔루션 탐색 시뮬레이션을 수행한다. 사용자에게 맞는 인터벤션을 제공하여, 감정노동자의 환경에 맞게 모달리티와 객체의 구성이 가능하고 사용자의 피드백에 따라 개선 가능한 개인 맞춤형 정신건강 위험 예측을 위한 멘탈 헬스케어 디지털 트윈 솔루션을 제공한다.

• 대한간호학회지
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• 제35권2호
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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.

• 디지털융복합연구
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• 제15권2호
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• pp.251-262
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• 2017

본 연구는 Bronfenbrenner의 생태학적 체계이론을 바탕으로 대학생의 정신건강에 영향을 미치는 요인을 구분하고 통합적인 모형 구축과 정신건강 증진을 위한 간호중재 개발의 기틀을 마련하고자 한다. 연구방법은 횡단적 조사연구로 대학생을 대상으로 하였다. 일반적 특성 및 정신건강관련 특성은 SPSS 20.0 프로그램을, 모형의 적합도검증, 가설검증은 Amos 20.0 프로그램을 이용하였다. 연구결과에서 모형의 적합도 지수는 $x^2=614.90$(p=.000), Q값=3.5, GFI=.88, AGFI=.84, NFI=.92, NNFI=.94, CFI=.94, RMR=.02, RMSEA=.08 적합도를 나타내어 연구모형이 가정에 적합한 것으로 나타났다. 연구결과 정신건강에 스트레스가 가장 높은 영향을 미치고 있으며 스트레스 대처 방안과 자아 존중감, 부모의 양육태도가 정신건강에 영향을 미치는 것으로 확인되었다. 대학생의 정신건강을 증진시키기 위해서는 스트레스 관리, 자아존중감 증진, 스트레스 대처 능력 향상을 도모하는 간호중재를 개발하고, 학교현장 및 정신보건 실무에 활용하는 중재가 수행되어야 할 것으로 생각된다.

• 한국산학기술학회논문지
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• 제19권9호
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• pp.243-250
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• 2018

본 연구는 대학생의 자아존중감, 대학생활 스트레스 및 취업스트레스가 정신건강에 영향을 미치는 요인을 분석하기 위한 서술적 조사연구이다. 연구대상자는 대학생 312명을 대상으로 설문조사 하였다. 자료분석은 t-test, one-way 분산분석, $Scheff{\acute{e}}$ test, 상관분석 및 다중회귀분석을 이용하였다. 5점 Likert 척도로 측정된 정신건강, 대학생활 스트레스 및 취업스트레스의 평균치는 각각 1.69, 1.87, 2.21이었다. 4점 Likert 척도로 측정된 자아존중감의 평균치는 2.79였다. 성별과 친구 수에 따른 정신건강 정도는 통계적으로 유의한 영향을 미쳤다. 자아존중감(r=-.426, p<0.001)은 정신건강과 통계적으로 유의한역 상관관계를 보였으며, 대학생활 스트레스(r=.660, p<0.001)와 취업스트레스(r=.517, p<0.001)는 통계적으로 유의한 정상관관계를 보였다. 다중회귀 분석을 한 결과, 대학생의 정신건강에 가장 영향을 미치는 요인은 대학생활 스트레스(${\beta}=.545$)이었고, 자아존중감(${\beta}=-.145$), 취업스트레스(${\beta}=0.069$) 순이었으며, 3가지 연구변인의 설명력은 45.2 %였다. 대학생의 정신건강을 개선하기 위해서는 대학생활 스트레스와 취업스트레스를 낮추고 자아존중감을 높일 수 있는 대학 내 프로그램을 모색해야 할 필요가 있다.

• 간호행정학회지
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• 제16권3호
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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.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 추계학술대회논문집
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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.