• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.284-310
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• 2024

Identifying clinical pathways for disease diagnosis and treatment process recommendations are seriously decision-intensive tasks for health care practitioners. It requires them to rely on their expertise and experience to analyze various categories of health parameters from a health record to arrive at a decision in order to provide an accurate diagnosis and treatment recommendations to the end user (patient). Technological adaptation in the area of medical diagnosis using AI is dispensable; using expert systems to assist health care practitioners in decision-making is becoming increasingly popular. Our work architects a novel knowledge-based recommender system model, an expert system that can bring adaptability and transparency in usage, provide in-depth analysis of a patient's medical record, and prescribe diagnostic results and treatment process recommendations to them. The proposed system uses a set of parallel discrete fuzzy rule-based classifier systems, with each of them providing recommended sub-outcomes of discrete medical conditions. A novel knowledge-based combiner unit extracts significant relationships between the sub-outcomes of discrete fuzzy rule-based classifier systems to provide holistic outcomes and solutions for clinical decision support. The work establishes a model to address disease diagnosis and treatment recommendations for primary lung disease issues. In this paper, we provide some samples to demonstrate the usage of the system, and the results from the system show excellent correlation with expert assessments.

• Journal of Physiology & Pathology in Korean Medicine
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• v.28 no.2
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• pp.243-250
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• 2014

This study examined the knowledge and understanding of integrative medicine in Korea, specifically conflicts between western and oriental medicine within Parallel (Dual) health care systems. Qualitative methodology using grounded theory guided semi-structured, in-depth interviews with Western Medical Doctors (W.M.D., n = 6), Oriental Medical Doctors (O.M.D., n = 5) and Traditional Chinese Medicine Practitioners (T.C.M.P., n = 4). Thematic analysis was used to determine broad themes from the interviews. 15 professionals (W.M.D. (40%), O.M.D. (33%), T.C.M.P. (27%), 10 males (67%) and 5 females (33%), mean age 45) were interviewed, recorded, and transcribed. Thematic analysis revealed three key themes: systematic conflicts, integration and future directions. Subthemes of systematic conflicts included: credibility of Oriental Medicine, commercial imperatives, maintaining social standing of O.M.D., professional qualifications and lack structures supporting collaborative practice. Integration subthemes included lack of academic linkage and clarity for appropriate triage, opposing medical paradigms and limited social imperative. Future directions should include: social justification, guarantee of oriental medicine legitimacy, role of government and understanding of scientific evidence. To successfully integrate dual medical systems there is a need to address differences in social-environmental factors and perceptions of scientific understanding, as well as developing strong academic links in clinical practice.

• Child Health Nursing Research
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• v.26 no.1
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• pp.72-81
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• 2020

Purpose: This study was conducted to investigate nurses' perceptions and performance of family-centered care (FCC) at a children's hospital in Sri Lanka and to explore the feasibility of implementing FCC in the context of the Sri Lankan healthcare system. Methods: A convergent, parallel, mixed-methods design was applied to understand Sri Lankan nurses' perspectives on FCC. In total, 157 nurses working at a large teritagy children's hospital responded to a self-report survey and 18 nurses participated in focus group interviews. Results: Of the factors of FCC, family participation in caring for children received the highest score (4.09±0.51) for perceptions, and information-sharing received the highest score (3.54±0.55) for performance. The qualitative data revealed the following five themes: (a) importance of the family in caring for children; (b) helping families during children's hospitalization; (c) taking steps to implement FCC, even with imperfect knowledge; (d) barriers in the current situation; and (e) suggested strategies to promote FCC. Conclusion: Participants endorsed the concept of FCC and demonstrated some aspects of it in their day-to-day practice. The results indicate a clear knowledge deficit and several challenges, which need to be addressed to effectively implement FCC.

• Smart Structures and Systems
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• v.6 no.3
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• pp.309-333
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• 2010

Civil infrastructure, in both its construction and maintenance, represents the largest societal investment in this country, outside of the health care industry. Despite being the lifeline of US commerce, civil infrastructure has scarcely benefited from the latest sensor technological advances. Our future should focus on harnessing these technologies to enhance the robustness, longevity and economic viability of this vast, societal investment, in light of inherent uncertainties and their exposure to service and even extreme loadings. One of the principal means of insuring the robustness and longevity of infrastructure is to strategically deploy smart sensors in them. Therefore, the objective is to develop novel, durable, smart sensors that are especially applicable to major infrastructure and the facilities to validate their reliability and long-term functionality. In some cases, this implies the development of new sensing elements themselves, while in other cases involves innovative packaging and use of existing sensor technologies. In either case, a parallel focus will be the integration and networking of these smart sensing elements for reliable data acquisition, transmission, and fusion, within a decision-making framework targeting efficient management and maintenance of infrastructure systems. In this paper, prudent and viable sensor and health monitoring technologies have been developed and used in several large structural systems. Discussion will also include several practical bridge health monitoring applications including their design, construction, and operation of the systems.

• Journal of Preventive Medicine and Public Health
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• v.50 no.2
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• pp.91-99
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• 2017

Objectives: As in many low-income and middle-income countries, out-of-pocket (OOP) payments by patients or their families are a key healthcare financing mechanism in Bangladesh that leads to economic burdens for households. The objective of this study was to identify whether and to what extent socioeconomic, demographic, and behavioral factors of the population had an impact on OOP expenditures in Bangladesh. Methods: A total of 12 400 patients who had paid to receive any type of healthcare services within the previous 30 days were analyzed from the Bangladesh Household Income and Expenditure Survey data, 2010. We employed regression analysis for identify factors influencing OOP health expenditures using the ordinary least square method. Results: The mean total OOP healthcare expenditures was US dollar (USD) 27.66; while, the cost of medicines (USD 16.98) was the highest cost driver (61% of total OOP healthcare expenditure). In addition, this study identified age, sex, marital status, place of residence, and family wealth as significant factors associated with higher OOP healthcare expenditures. In contrary, unemployment and not receiving financial social benefits were inversely associated with OOP expenditures. Conclusions: The findings of this study can help decision-makers by clarifying the determinants of OOP, discussing the mechanisms driving these determinants, and there by underscoring the need to develop policy options for building stronger financial protection mechanisms. The government should consider devoting more resources to providing free or subsidized care. In parallel with government action, the development of other prudential and sustainable risk-pooling mechanisms may help attract enthusiastic subscribers to community-based health insurance schemes.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.663-664
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• 2006

This paper presents a reconfigurable digital signal processing(rDSP) architecture that is effective for implementing adaptive digital signal processing in the applications of smart health care system. This rDSP architecture employs an evolution capability of FIR filters using genetic algorithm. Parallel genetic algorithm based rDSP architecture evolves FIR filters to explore optimal configuration of filter combination, associated parameters, and structure of feature space adaptively to noisy environments for an adaptive signal processing. The proposed DSP architecture is implemented using Xilinx Virtex4 FPGA device and SMIC 0.18um CMOS Technology.

• The Korean Journal of Health Service Management
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• v.9 no.3
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• pp.81-93
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• 2015

Objectives : The objective of this study was to examine preceding variables that affect the dual commitment of outsourced employees working at hospitals and to analyze the influence of these variables on job performance. Methods : Data were collected from 461 outsourced employees, working at 7 general hospitals, which had introduced the outsourcing system, using a structured, self-administered questionnaires. Frequency, validity/reliability, correlation and path analyses were done for data analysis. Results : The results of the path analyses showed that both commitment to the hiring company and commitment to the client company (hospital) had statistically significant positive effects on job performance. Additionally, when the 'single measurement' approach was used, dual commitment had a larger positive effect, compared with the 'parallel approach.' Among the preceding variables, 'satisfaction for the job itself' was found to be the most important variable affecting dual commitment and job performance. Conclusions : In conclusion, to enhance the job performance of outsourced employees, it is important for management to examine and improve the various factors related to job satisfaction. Additonally, for outsourced employees to have organizational commitments to the hiring and client companies simultaneously, management should emphasize a sense of unity and share organizational values.

• Journal of Society of Preventive Korean Medicine
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• v.6 no.2
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• pp.36-47
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• 2002

Over thousands of years oriental traditional medicine has developed a theoretical and practical approach to treat and prevent diseases and to promote people's health in China and Korea. In China, the integration of traditional Chinese medicine into the national healthcare system began in the late 1950s. This was in response to national planning needs to provide comprehensive healthcare services. On contrary to China, South Korea established the parallel operation of two independent medical systems in 1952. Hence there has been a political conflict between oriental and modern medicine over issues of fee, the ability to sell and prescribe herbal medicines, and the licensing of practitioners in traditional medicines. Given this background. This study is to compare peoples' attitudes and opinions for oriental traditional medicine by ethnicity (Chinese, Korean-Chinese and Korean). Chinese and Korean-Chinese were more used and satisfied with traditional medicine treatment and traditional practitioners compared with Koreans. The proportion of Koreans who reported the cost of traditional treatments was expensive was higher than those of Chinese and Korean Chinese. Most of Chinese, Korean-Chinese, and Koreans reported that they would use traditional medicine: 1) when they would have some disease to be treated best through traditional medicine; and 2) when traditional practitioner had a reputation and lots of experiences for those diseases. Most Korean people reported that oriental and western practitioners should cooperate each other to improve the quality of care. Therefore, policy framework including integration of traditional and western medicine, regulation, etc. is needed. In addition, research is needed to determine which diseases is treated best through traditional medicine.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.361-366
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• 2023

Apache Hadoop, a representative framework for distributed processing of big data, has the advantage of increasing cluster size up to thousands of nodes to improve parallel distributed processing performance. However, reducing the size of the cluster is limited to the extent of permanently decommissioning nodes with defects or degraded performance, so there are limitations to operate multiple nodes flexibly in small clusters. In this paper, we discuss the problems that occur when removing nodes from the Hadoop cluster and propose a dynamic down-scaling technique to manage the distributed cluster more flexibly. To do this, we design and implement a modified Hadoop system and interfaces to support dynamic down-scaling of the cluster which supports temporary pause of a node and reconnection of it when necessary, rather than decommissioning the node when removing a node from the Hadoop cluster. We have verified that effective downsizing can be performed without performance degradation based on experimental results.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.