• Safety and Health at Work
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• v.2 no.4
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• pp.321-327
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• 2011

Objectives: To find out from an analysis of empirical data the levels of influence, which a labor union (LU) and Occupational Safety and Health Committee (OSHC) have in reducing the occupational injury and illness rate (OIIR) through their accident prevention activities in manufacturing industries with five or more employees. Methods: The empirical data used in this study are the Occupational Safety and Health Tendency survey data, Occupational Accident Compensation data and labor productivity and sales data for the years 2003 to 2007. By matching these three sources of data, a final data set (n = 280) was developed and analyzed using SPSS version 18 (SPSS Inc., Chicago, IL, USA). Results: It was found that a workplace with a LU has a lower OIIR than one without a LU. In manufacturing industries with five or more employees in 2007, the OIIR of the workplaces without a LU was 0.87%, while that of workplaces with a LU was much lower at 0.45%. In addition, workplaces with an established OSHC had a lower OIIR than those without an OSHC. Conclusion: It was found that the OIIR of workplaces with a LU is lower than those without a LU. Moreover, those with the OSHC usually had a lower OIIR than those without. The workplace OIIR may have an impact on management performance because the rate is negatively correlated with labor productivity and sales. In the long run, the OIIR of workplaces will be reduced when workers and employers join forces and recognize that the safety and health activities of the workplace are necessary, not only for securing the health rights of the workers, but also for raising labor productivity.

• Journal of Platform Technology
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• v.10 no.1
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• pp.29-34
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• 2022

The emergence of new diseases such as the Covid 19 pandemic that occurs in the 21st century and the occurrence of health abnormalities according to the busy daily life of modern people are increasing. Accordingly, the importance of health care management and data-based health management is being highlighted, and in particular, interest in personal health management data based on personal health care data of patients is rapidly increasing. In this study, to solve the difficult problems of personal health management, we developed a personal health care platform incorporating IT for self-diagnosis and solution and developed an application that measures bio-signals generated in the human body and transmits them to the platform. A health management system was established. Through this, not only the health care of modern people, but also the psychological and emotional care support needs through psychological and emotional monitoring of the developmentally disabled and the vulnerable who have difficulty in expressing their opinions are to be addressed. In addition, the overall health and living environment data of the individual was integrated to develop an optimized medical and health management service for the individual.

• Journal of Korean Academy of Oral Health
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• v.42 no.4
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• pp.101-108
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• 2018

Objectives: The Korea National Health and Nutrition Examination Survey (KNHANES) is a national surveillance system that has been assessing the health and nutritional status of Koreans since 1998. Based on the National Health Promotion Act, the surveys have been conducted by the Korea Centers for Disease Control and Prevention (KCDC). Methods: An oral examination as part of The National Health and Nutrition Examination was proposed to calculate the sample design and survey participation. The surveying system was presented by classifying the measurement environment, screening, and survey items by year, and the merits and limitations of using the data were suggested by examining the status of survey quality management and the process of disclosing raw data. Results: This nationally representative cross-sectional survey samples approximately 10,000 individuals each year and collects information on oral examinations and oral health interviews. Data for the oral health component of KNHANES was obtained to assess the oral health status of Koreans and determine the prevalence of dental caries and periodontitis. The oral health data quality control of KNHANES was composed of three parts: "Education Program" and "Field Training Program" for quality control of oral health examiners (dentists) by the professional academy, and "Data management" by the KCDC. After completion of the three-step data check, the indicators of dental caries, periodontal disease, and oral health behavior were published in the National Health Statistics. Conclusions: To achieve the goals of oral health indicators, we will continue to monitor so that we can use it as basic data for oral policies and carry out various linkage analyses related to oral diseases.

• Journal of Preventive Medicine and Public Health
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• v.56 no.1
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• pp.59-66
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• 2023

Objectives: The aim of this study was to evaluate the effect of body weight status and sleep duration on the discrete-time hazard of menarche in Korean schoolgirls using multiple-point prospective panel data. Methods: The study included 914 girls in the 2010 Korean Children and Youth Panel Study who were in the elementary first-grader panel from 2010 until 2016. We used a Gompertz regression model to estimate the effects of weight status based on age-specific and sex-specific body mass index (BMI) percentile and sleep duration on an early schoolchild's conditional probability of menarche during a given time interval using general health condition and annual household income as covariates. Results: Gompertz regression of time to menarche data collected from the Korean Children and Youth Panel Study 2010 suggested that being overweight or sleeping less than the recommended duration was related to an increased hazard of menarche compared to being average weight and sleeping 9 hours to 11 hours, by 1.63 times and 1.38 times, respectively, while other covariates were fixed. In contrast, being underweight was associated with a 66% lower discrete-time hazard of menarche. Conclusions: Weight status based on BMI percentiles and sleep duration in the early school years affect the hazard of menarche.

• Journal of Korean Medicine for Obesity Research
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• v.17 no.1
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• pp.46-53
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• 2017

Health care big data is thought to be a promising field of interest for disease prediction, providing the basis of medical treatment and comparing effectiveness of different treatments. Korean government has begun an effort on releasing public health big data to improve the quality and safety of medical care and to provide information to health care professionals. By studying population based big data, interesting outcomes are expected in many aspects. To initiate research using health care big data, it is crucial to understand the characteristics of the data. In this review, we analyzed cases from inside and outside the country using clinical data registry. Based on successful cases, we suggest research method for evidence-based Korean medicine. This will provide better understanding about health care big data and necessity of Korean medicine data registry network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.974-992
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• 2021

Recently, the healthcare field has undergone rapid changes owing to the accumulation of health big data and the development of machine learning. Data mining research in the field of healthcare has different characteristics from those of other data analyses, such as the structural complexity of the medical data, requirement for medical expertise, and security of personal medical information. Various methods have been implemented to address these issues, including the machine learning model and cloud platform. However, the machine learning model presents the problem of opaque result interpretation, and the cloud platform requires more in-depth research on security and efficiency. To address these issues, this paper presents a recent technology for Internet-of-Things-based (IoT-based) health big data processing. We present a cloud-based IoT health platform and health big data processing technology that reduces the medical data management costs and enhances safety. We also present a data mining technology for health-risk prediction, which is the core of healthcare. Finally, we propose a study using explainable artificial intelligence that enhances the reliability and transparency of the decision-making system, which is called the black box model owing to its lack of transparency.

• Journal of Intelligence and Information Systems
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• v.6 no.1
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• pp.73-82
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• 2000

This study examined the characteristicso f the knowledge discovery and data mining algorithms to demonstrate how they can be used to predict health outcomes and provide policy information for hypertension management using the Korea Medical Insurance Corporation database. Specifically this study validated the predictive power of data mining algorithms by comparing the performance of logistic regression and two decision tree algorithms CHAID (Chi-squared Automatic Interaction Detection) and C5.0 (a variant of C4.5) since logistic regression has assumed a major position in the healthcare field as a method for predicting or classifying health outcomes based on the specific characteristics of each individual case. This comparison was performed using the test set of 4,588 beneficiaries and the training set of 13,689 beneficiaries that were used to develop the models. On the contrary to the previous study CHAID algorithm performed better than logistic regression in predicting hypertension but C5.0 had the lowest predictive power. In addition CHAID algorithm and association rule also provided the segment characteristics for the risk factors that may be used in developing hypertension management programs. This showed that data mining approach can be a useful analytic tool for predicting and classifying health outcomes data.

• Steel and Composite Structures
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• v.49 no.6
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• pp.701-711
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• 2023

In order to fully reflect variation characteristics of composite concrete dam health state, the monitoring data is applied to diagnose composite concrete dam health state. Composite concrete dam lesion development to wreckage is a precursor, and its health status can be judged. The monitoring data are generally non-linear and unsteady time series, which contain chaotic information that cannot be characterized. Thus, it could generate huge influence for the construction of monitoring models and the formulation of corresponding health diagnostic indicators. This multi-scale diagnosis process is from point to whole. Chaotic characteristics are often contained in the monitoring data. If chaotic characteristics could be extracted for reflecting concrete dam health state and the corresponding diagnostic indicators will be formulated, the theory and method of diagnosing concrete dam health state can be huge improved. Therefore, the chaotic characteristics of monitoring data are considered. And, the extracting method of the chaotic components is studied from monitoring data based on fuzzy dynamic cross-correlation factor method. Finally, a method is proposed for formulating composite concrete dam health state indicators. This method can effectively distinguish chaotic systems from deterministic systems and reflect the health state of concrete dam in service.

• Safety and Health at Work
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• v.10 no.1
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• pp.114-121
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• 2019

Background: The photolithography process in the semiconductor industry uses various chemicals with little information on their constitution. This study aimed to identify the chemical constituents of photoresist (PR) products and their by-products and to compare these constituents with material safety data sheets (MSDSs) and analytical results. Methods: A total of 51 PRs with 48 MSDSs were collected. Analysis consisted of two parts: First, the constituents of the chemical products were identified and analyzed using MSDS data; second, for verification of the by-products of PR, volatile organic compounds were analyzed. The chemical constituents were categorized according to hazards. Results: Forty-five of 48 products contained trade secrets in amounts ranging from 1 to 65%. A total of 238 ingredients with multiple counting (35 ingredients without multiple counting) were identified in the MSDS data, and 48.7% of ingredients were labeled as trade secrets under the Korea Occupational Safety and Health Act. The concordance rate between the MSDS data and the analytical result was 41.7%. The by-product analysis identified 129 chemicals classified according to Chemical Abstracts Service No., with 17 chemicals that are carcinogenic, mutagenic, and reprotoxic substances. Formaldehyde was found to be released from 12 of 21 products that use novolak resin. Conclusion: We confirmed that several PRs contain carcinogens, and some were not specified in the toxicological information in the MSDS. Hazardous chemicals, including benzene and formaldehyde, are released from PRs products as by-products. Therefore, it is necessary to establish a systematic management system for chemical compounds and the working environment.

• Safety and Health at Work
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• v.13 no.1
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• pp.112-116
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• 2022

Background/Aims: The Gachon Regional Occupational Cohort Study (GROCS) is a large-scale longitudinal study of occupational safety and health data (covering Work Environment Monitoring, Workers' Health Surveillance, and Occupational Health Service) conducted by the Gachon University Gil Medical Center (GUGMC) in Incheon, Republic of Korea. We conducted GROCS to identify the health effects of workers' occupational risks, behavior, socioeconomic status, and life style. Methods: The GROCS includes data from Work Environment Monitoring, Workers' Health Surveillance, and Occupational Health Service. The baseline year for all data collection was 2018. Work Environment Monitoring was conducted in 240 companies located in Incheon. General Health Examination and Special Health Examination were performed on 32,725 and 9,504 workers, respectively. Occupational Health Services were provided to 16,883 workers in 171 companies. These data have been collected and operated at an external data management institution and were provided as a retrospective cohort after removing personal identification information. Results: In 2018, the total number of companies was 2,854, among which which 488 special Health Examination, 171 Work Environment Monitoring, and 240 Occupational Health Service. The proportion of companies undergoing Special Health Examination was 17.1%, the proportion of companies undergoing Work Environment Monitoring was 8.4%, and the proportion of Companies undergoing Occupational Health Service was 6.0%. Conclusion: GROCS expects researchers to utilize its useful and reliable resource for occupational health and surveillance with for academic or political purposes to lead to improved workers' health and working environment.