• Information Systems Review
• /
• v.25 no.1
• /
• pp.165-187
• /
• 2023

The continuous increase in life expectancy and high interest in health has brought about significant changes in the use of health information by the public according to the development of information technology represented by the Internet and smartphones. As the medical market expands to the mobile health environment, many health-related apps have been created and distributed, but the acceptance rate is slow as it has become challenging to provide services due to various regulations. In this study, perceived value, perceived risk factors (psychological risk, risk of time-loss, legal risk), and perceived benefits (usefulness, interaction, autonomy) were derived and verified as factors that affect the acceptance resistance of personal health record apps based on the privacy calculation model. In addition, by analyzing the moderating effect of trust in the manufacturer, how the perceived risk and perceived benefit affect the perceived value was verified. A survey was conducted on Korean college students who recognized the personal health record apps but did not use them, and 127 samples were analyzed using structural equations. As a result of hypothesis verification, perceived value has a negative effect on acceptance resistance, perceived risk (risk of time-loss) has a negative effect on perceived value, and perceived benefits (usefulness, interaction, autonomy) were found to have a positive effect on perceived value. Trust in manufacturers has weakened the impact of perceived risks (legal risk) on perceived values. This study is expected to play an important role in maintaining a competitive advantage in the personal health record app market environment by identifying and proposing detailed criteria for reducing the acceptance resistance of personal health record apps.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.1
• /
• pp.16-30
• /
• 2023

The elderly population is increasing owing to a low fertility rate and an aging population. In addition, life expectancy is increasing, and the advancement of medicine has increased the importance of health to most people. Therefore, government and companies are developing and supporting smart healthcare, which is a health-related product or industry, and providing related services. Moreover, with the development of the Internet, many people are managing their health through online searches. The most convenient way to achieve such management is by consuming nutritional supplements or seasonal foods to prevent a nutrient deficiency. However, before implementing such methods, knowing the nutrient status of the individual is difficult, and even if a test method is developed, the cost of the test will be a burden. To solve this problem, we developed a questionnaire related to nutrient classification twice, based upon which an adaptive algorithm was designed. This algorithm was designed as a machine learning based algorithm for nutrient classification and its accuracy was much better than the other machine learning algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.8
• /
• pp.1460-1465
• /
• 2016

Lots of costs threaten the sustainability of the national health-guarantee system. Despite research by the national center for disease control and prevention on health care dynamics with its auditing systems, there are still restrictions of time limitation, sample limitation, and, target diseases limitation. Against this backdrop, using huge volume of total data, many technologies could be fully adopted to the preliminary forecasting and its target-disease expanding of health. With structured data from the national health insurance and unstructured data from the social network service, we attempted to design a model to predict disease. The model can enhance national health and maximize social benefit by providing a health warning service. Also, the model can reduce the advent increase of national health cost and predict timely disease occurrence based on Big Data analysis. We researched related medical prediction cases and performed an experiment with a pilot project so as to verify the proposed model.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.12
• /
• pp.5855-5877
• /
• 2017

In view of personal health and disease management based on cost effective healthcare services, there is a growing need for real-time monitoring services. The electrocardiogram (ECG) signal is one of the most important of health information and real-time monitoring of the ECG can provide an efficient way to cope with emergency situations, as well as assist in everyday health care. In this system, it is essential to continuously collect and transmit large amount of ECG data within a given time and provide maximum user convenience at the same time. When considering limited wireless capacity and unstable channel conditions, appropriate signal processing and transmission techniques such as compression are required. However, ISO/IEEE 11073 standards for interoperability between personal health devices cannot properly support compressed data transmission. Therefore, in the present study, the problems for handling compressed data are specified and new extended agent and manager are proposed to address the problems while maintaining compatibility with existing devices. Extended devices have two PM-stores enabling compression and a novel transmission scheme. A variety of compression techniques can be applied; in this paper, discrete cosine transformation (DCT) is used. And the priority of information after DCT compression enables new transmission techniques for performance improvement. The performance of the compressed signal and the original uncompressed signal transmitted over the noisy channel are compared in terms of percent root mean square difference (PRD) using our simulation results. Our transmission scheme shows a better performance and complies with 11073 standards.

• Nutrition Research and Practice
• /
• v.11 no.1
• /
• pp.76-76
• /
• 2017

• 대한예방의학회:학술대회논문집
• /
• 1994.02b
• /
• pp.129-136
• /
• 1994

A model was developed for a simple clinical trial in which graders had defined probabilities of misclassifying pathologic material to disease present or absent. The authors compared Kappa between graders, and efficiency and bias in the clinical trial in the presence of misclassification. Though related to bias and efficiency, Kappa did not predict these two statistics well. These results pertain generally to evaluation of systems for encoding medical information, and the relevance of Kappa in determining whether such systems are ready for use in comparative studies. The authors conclude that, by itself, Kappa is not informative Enough to evaluate the appropriateness of a grading scheme for comparative studies. Additional, and perhaps difficult, questions must be addressed for such evaluation.

• Journal of Korean Society of Occupational and Environmental Hygiene
• /
• v.30 no.3
• /
• pp.280-291
• /
• 2020

Objectives: Hazard classification is a controversial issue in the new MSDS system in which chemical companies have to prepare and submit MSDS for chemicals that they manufacture or import to the competent authorities according to the amended Occupational Safety and Health Act. The aim of this study is to suggest how to apply and manage harmonized hazard classification criteria and results by investigating current hazard classification systems and trends. Methods: The domestic issues about different hazard classification criteria and results were investigated by reviewing the literature and business outcomes regarding KOSHA. We also checked official and unofficial reports from the UN to understand international discussion about the topic. Chemical hazard classification results from agencies providing chemical information were analyzed to compare a harmonized rate between classifications. Furthermore, a field survey of a few chemical companies was conducted. Results: Under the related competent authorities, an integrated standard proposal was developed to harmonize the domestic hazard classification criteria. Although harmonized chemical information is strongly needed, we recognized the uncertainty and difficulty of harmonized hazard classification from the UN global list project review. In practice the harmonization rate of the classification was generally low between the classification in KOSHA, MoE, and EU CLP. Among hazard classes, health hazards largely led the disharmony. The field survey revealed a change of perception that the main body of chemical information production is manufacturers. Approaches and solutions about hazard classification issues differed depending on business size, types of chemical handling, and other factors. Conclusions: We proposed reasonable ways by time and step to apply hazard classification in the new MSDS system. Chemical manufacturers should make and offer chemical information including responsible hazard classifications. The government should primarily accept these classifications, evaluate them by priority, and support or supervise workplaces in order to communicate reliable chemical information.

• Tuberculosis and Respiratory Diseases
• /
• v.86 no.1
• /
• pp.23-32
• /
• 2023

Everyone is aware that air and environmental pollutants are harmful to health. Among them, indoor air quality directly affects physical health, such as respiratory rather than outdoor air. However, studies that have examined the correlation between environmental and health information have been conducted with public data targeting large cohorts, and studies with real-time data analysis are insufficient. Therefore, this research explores the research with an indoor air quality monitoring (AQM) system based on developing environmental detection sensors and the internet of things to collect, monitor, and analyze environmental and health data from various data sources in real-time. It explores the usage of wearable devices for health monitoring systems. In addition, the availability of big data and artificial intelligence analysis and prediction has increased, investigating algorithmic studies for accurate prediction of hazardous environments and health impacts. Regarding health effects, techniques to prevent respiratory and related diseases were reviewed.

• Journal of Preventive Medicine and Public Health
• /
• v.27 no.3 s.47
• /
• pp.475-486
• /
• 1994

In order to provide necessary information for better health of workers through understanding the actual status of the industries adopting shift systems. The data were gathered from a stratified random sample of 347 (0.5%) firms selected out of about 70,000 manufacturing industries throughout the nation in 1993. Stratification during sampling was by industrial group and number of workers. The major findings obtained from 288 firms surveyed completely were as follows : 1. About 20.2% of the 288 firms were adopting shift systems and shirtworkers accounted for about 25.1% of the total work force in 288 firms. 2. The bigger number of workers was, the higher the adopting rate of shift system was. 3. The rate of having welfare facilities such as dining room, commuting bus, washing facilities, and health care room etc. was higher in the industries adopting shift systems. 4. The major industrial groups adopting shift systems were the rubber a: plastic goods manufacturing industry (54.1 per 100 firms) and the textile manufacturing industry (44.8 per 100 firms). However, the proportion of shiftworkers was higher in the textile manufacturing industry (70.5 per 100 firms) and the electronics industry (57.9 per 100 frms). 5. The most predominant work schedule was the weekly rotating, semi-continuos 2-crew 2-shift system (47.5%). 6. In the industries adopting shift systems, shiftworkers had an adjusted average of 0.29 spells per 100 workers ($0.14{\sim}0.45$ in 95% C.I.) compared to 0.23 spells per 100 nonshift dayworkers ($0.15{\sim}0.31$ in 95% C.I.) for 1 month. 7. Also, in the industries adopting shift systems, the adjusted average annuel turn-over rate of shiftworkers was 13.07 per 100 workers ($12.03{\sim}14.12$ in 95% C.I.) compared to 10.18 per 100 nonshift dayworkers ($9.53{\sim}10.82$ in 95% C.I.).

• Smart Structures and Systems
• /
• v.2 no.1
• /
• pp.25-46
• /
• 2006

Aging wiring in buildings, aircraft and transportation systems, consumer products, industrial machinery, etc. is among the most significant potential causes of catastrophic failure and maintenance cost in these structures. Smart wire health monitoring can therefore have a substantial impact on the overall health monitoring of the system. Reflectometry is commonly used for locating faults on wire and cables. This paper compares Time domain reflectometry (TDR), frequency domain reflectometry (FDR), mixed signal reflectometry (MSR), sequence time domain reflectometry (STDR), spread spectrum time domain reflectometry (SSTDR) and capacitance sensors in terms of their accuracy, convenience, cost, size, and ease of use. Advantages and limitations of each method are outlined and evaluated for several types of aircraft cables. The results in this paper can be extrapolated to other types of wire and cable systems.