• 약학회지
• /
• 제54권4호
• /
• pp.309-315
• /
• 2010

The Positive List System was newly introduced in South Korea as of January 2007. This study aims to survey and compare perception of and attitudes toward the Positive List System in the process of new drug listing that health professionals and pharmaceutical companies have. 50 professionals and 52 companies answered the questionnaire regarding health policy environments, policy decision/enforcement process, policy effects and satisfaction related to introducing the Positive List System. SAS 9.1 was used for statistical analyses. The results showed that participants had the general sympathy with health policy environments for the introduction of the Positive List System into South Korea. However, the response rates of policy decision/enforcement process and effects were negative and these tendencies were more striking in pharmaceutical companies. As for policy satisfaction, participants marked positive responses more than negative ones. It is necessary to remedy and supplement problems with policy decision/enforcement policy and effects revealed in this study and to improve the Positive List System through gathering opinions among groups and organization concerned.

• 한국정보통신학회논문지
• /
• 제25권11호
• /
• pp.1519-1524
• /
• 2021

본 논문에서는 퍼지 의사 결정 트리를 이용한 한의학 기반의 건강 사전 진단 시스템을 제안한다. 제안된 퍼지 의사결정 트리 기반 한방 사전 진단 방법은 과거의 데이터를 미리 학습시킨 후에 엔트로피에 따라 경계 값을 계산한 후, 사용자가 여러 증상을 선택하면 선택된 증상에 해당되는 상위 질병 5개를 도출한다. 그리고 도출된 상위 5개의 질병과 도출된 질병의 원인과 민간요법을 제공한다. 질병과 증상에 대한 데이터베이스는 한의사가 추천한 여러 한의학 전문 서적을 기반으로 증상과 질병의 데이터베이스를 설계하고 한의학 전문의가 검증한 후에 구현하였다. 과거의 데이터를 바탕으로 증상을 학습함으로써 제안된 한의학 기반 건강 사전 진단 시스템 방법은 보다 정확한 진단 결과를 더 빨리 제공할 수 있다.

• Journal of Communications and Networks
• /
• 제11권4호
• /
• pp.350-358
• /
• 2009

In a wireless sensor network (WSN) setting, this paper presents a distributed decision-making framework and illustrates its application in an online structural health monitoring (SHM) system. The objective is to recover a damage severity vector, which identifies, localizes, and quantifies damages in a structure, via distributive and collaborative decision-making among wireless sensors. Observing the fact that damages are generally scarce in a structure, this paper develops a nonlinear 0-norm minimization formulation to recover the sparse damage severity vector, then relaxes it to a linear and distributively tractable one. An optimal algorithm based on the alternating direction method of multipliers (ADMM) and a heuristic distributed linear programming (DLP) algorithm are proposed to estimate the damage severity vector distributively. By limiting sensors to exchange information among neighboring sensors, the distributed decision-making algorithms reduce communication costs, thus alleviate the channel interference and prolong the network lifetime. Simulation results in monitoring a steel frame structure prove the effectiveness of the proposed algorithms.

• 응용통계연구
• /
• 제27권5호
• /
• pp.667-679
• /
• 2014

우리나라 노인장기요양보험에서는 수급자가 월 한도액 범위 내에서 필요한 서비스를 비용-효율적으로 이용할 수 있도록 지원하고자 표준장기요양이용계획서를 작성하여 제공하고 있다. 본 연구는 표준장기요양이용계획서의 객관성 확보와 업무 효율성 제고를 위하여 의사결정나무기법을 이용해 수급자의 건강 및 기능 상태에 맞는 최적의 급여계획을 도출하는 표준급여모형을 개발하였다. 타당도 높은 모형 개발을 위하여 국민건강보험공단의 전국 220개 장기요양운영센터로부터 장기요양인정조사와 표준장기요양이용계획서 작성 경험이 풍부한 직원(본 연구에서는 '훈련된 조사자'라고 함)을 추천받아 자료수집의 내용과 방법에 대해 교육을 실시하였고, 이들이 수급자의 건강 및 기능 상태를 평가하고 작성한 수급자 개인별 맞춤형 급여계획을 자료 분석에 활용하였다. 표준급여모형은 1단계로 시설 또는 재가 급여 권고 여부를 결정하는 모형을, 2단계로 재가급여를 권고했을 경우의 재가급여 세부 종류별 권고 여부를 결정하는 모형을 개발하였다. 본 연구에서 개발된 표준급여모형은 전산프로그램화 되어 국민건강보험공단 직원이 수급자에게 제공할 표준장기요양이용계획을 수립하는 과정에 실제로 활용되고 있어 표준장기요양이용계획서의 객관성 확보와 업무 효율화가 기대된다.

• Journal of Preventive Medicine and Public Health
• /
• 제22권1호
• /
• pp.57-64
• /
• 1989

A decision support system (DSS) was developed to support doctor's decision-making in diagnosing hearing loss. The final diagnosis encompassed 41 diseases with the problem of hearing loss. The system was developed by integrating model-oriented DSS technique and artificial intelligence technology. The system can be used as both diagnosis tool and teaching tool for medical students. Furthermore, the AI technology obtained from this study may also be used in developing DSS for hospital management.

• 보건행정학회지
• /
• 제24권3호
• /
• pp.205-218
• /
• 2014

Background: Most studies on the national health insurance benefit expansion policy have focused on policy tools or decision-making process. Hence there was not enough understanding on how policies are actually implemented within the specific policy context in Korea which has a national mandatory health insurance system with a dominant proportion of private providers. The main objectives of this study is to understand the implementation process of the benefit coverage expansion policy. Unlike other implementation studies, we tried to examine both the process of implementation and decision making and how they interact with each other. Methods: Interviews were conducted with the ex-members of the Health Insurance Policy Review Committee. Medical doctors who implement the policy at the 'street-level' were also interviewed. To figure out major variables and the degree of their influences, the data were analyzed with Winter's Policy Implementation Model which integrates the decision making and implementation phases. Results: As predicted by the Winter model, problems in the decision making phase, such as conflicts among the members of committee, lack of applicable causal theories application of highly symbolic activities, and limited attention of citizen to the issue are key variables that cause the 'implementation failure.' In the implementation phase, hospitals' own financial interests and practitioners' dependence on the hospitals' guidance were barriers to meeting the policy goals of providing a better coverage for patients. Patients, the target group, tend to prefer physicians who prescribe more treatment and medicine. To note, 'fixers' who can link and fill the gap between the decision-makers and implementers were not present. Conclusion: For achieving the policy goal of providing a better and more coverage to patients, the critical roles of medical providers as street-level implementers should be noted. Also decision making process of benefit package expansion policy should incorporate its influence on the implementation phase.

• Nuclear Engineering and Technology
• /
• 제47권5호
• /
• pp.624-632
• /
• 2015

A self-diagnostic monitoring system is a system that has the ability to measure various physical quantities such as temperature, pressure, or acceleration from sensors scattered over a mechanical system such as a power plant, in order to monitor its various states, and to make a decision about its health status. We have developed a self-diagnostic monitoring system for an air-operated valve system to be used in a nuclear power plant. In this study, we have tried to improve the self-diagnostic monitoring system to increase its reliability. We have implemented three different machine learning algorithms, i.e., logistic regression, an artificial neural network, and a support vector machine. After each algorithm performs the decision process independently, the decision-making module collects these individual decisions and makes a final decision using a majority vote scheme. With this, we performed some simulations and presented some of its results. The contribution of this study is that, by employing more robust and stable algorithms, each of the algorithms performs the recognition task more accurately. Moreover, by integrating these results and employing the majority vote scheme, we can make a definite decision, which makes the self-diagnostic monitoring system more reliable.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제7권5호
• /
• pp.1036-1057
• /
• 2013

Wireless body area network (WBAN) is a promising candidate for future health monitoring system. Nevertheless, the path to mature solutions is still facing a lot of challenges that need to be overcome. Energy efficient scheduling is one of these challenges given the scarcity of available energy of biosensors and the lack of portability. Therefore, researchers from academia, industry and health sectors are working together to realize practical solutions for these challenges. The main difficulty in WBAN is the uncertainty in the state of the monitored system. Intelligent learning approaches such as a Markov Decision Process (MDP) were proposed to tackle this issue. A Markov Decision Process (MDP) is a form of Markov Chain in which the transition matrix depends on the action taken by the decision maker (agent) at each time step. The agent receives a reward, which depends on the action and the state. The goal is to find a function, called a policy, which specifies which action to take in each state, so as to maximize some utility functions (e.g., the mean or expected discounted sum) of the sequence of rewards. A partially Observable Markov Decision Processes (POMDP) is a generalization of Markov decision processes that allows for the incomplete information regarding the state of the system. In this case, the state is not visible to the agent. This has many applications in operations research and artificial intelligence. Due to incomplete knowledge of the system, this uncertainty makes formulating and solving POMDP models mathematically complex and computationally expensive. Limited progress has been made in terms of applying POMPD to real applications. In this paper, we surveyed the existing methods and algorithms for solving POMDP in the general domain and in particular in Wireless body area network (WBAN). In addition, the papers discussed recent real implementation of POMDP on practical problems of WBAN. We believe that this work will provide valuable insights for the newcomers who would like to pursue related research in the domain of WBAN.

• 보건행정학회지
• /
• 제16권1호
• /
• pp.54-72
• /
• 2006

This study was conducted as the primary work to develop a customer relationship management (CRM) system to improve the performance of health screening programs. The specific aims of the study was to identify and classify the characteristics of the people who did not receive their health screening using decision trees and to propose management strategies according to their characteristics identified. The data on a total of 5,102,761 subjects of health screening provided by the National Health Insurance Program in the year of 2002 were used. The target variable was whether they underwent their health screening. The input variables included a total of 27. The SAS 9.1 version was used for data preprocessing and statistical analyses. SAS Enterprise Miner was used to develop the decision trees model. The decision trees identified the factors greatly affecting the health screening. In the non-disease group, the highest rate of non-examinees was characterized by: no experience of receiving a health screen, household's age, non-insured episode for the last one year, and patients' age. In the disease group, the one showing the highest rate of non-examinees was characterized by: no experience of receiving a health screening, no experience of going to public health center or midwife clinic for the last one year, and examinees' age. Developing CRM systems for health screening management taking into account the individual characteristics would be considerably helpful to increase the rate of receiving health screening.

• 대한디지털의료영상학회논문지
• /
• 제9권1호
• /
• pp.17-19
• /
• 2007

This study applies in case of operating an exam using by the contrast order or inputting an order of a contrast media the exam of Radiology Department. It is developed for helping decision making as regards a process of an exam from reading the creatinine value automatically linked with Laboratory Information System. It can be confirmed by real-time information; therefore, the creditability of the information is able to be improved. We will create the base for Patient Monitoring System with the data from the side effect of the creatinine value and allergies. Decision Support System minimize the inconvenience and the riskiness of the given contrast medium for CT tests. We would like to improve medical services by providing a standard circumstance where patients are able to run tests safely and comfortably.