• Journal of the Korea Society of Computer and Information
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• v.22 no.10
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• pp.159-166
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• 2017

This study is a discussion of business process reengineering for the next generation information system. To be concrete, we analyze and reengineer the current business process in administration of academic, general and research. This reengineering is conformed by 5 steps, as analyzing environmental status that figures out factors of inside or outside (environmental analyzing), analyzing current condition that diagnoses AS-IS process (current situation analysis), establishing information-oriented vision with strategic accomplishing goals accordingly (visioning), deducing detailed improving tasks (defining tasks), and engineering TO-BE process comprehending improving matters (engineering model). This paper shows BPR model for next generation information system.

• Clinical and Experimental Pediatrics
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• v.53 no.9
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• pp.824-829
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• 2010

The safety and effectiveness of colonoscopy in the investigation of lower gastrointestinal tract pathology in children has been established for more than 2 decades in Korea. The skill and experience have since advanced to the point that both diagnostic and therapeutic colonoscopy are now routinely performed by most pediatric gastroenterologists. Pediatric colonoscopy differs significantly from its adult parallels in nearly every aspect including patient and parent management and preparation, selection criteria for sedation and general anesthetic, bowel preparation, expected diagnoses, instrument selection, imperative for terminal ileal intubation, and requirement for biopsies from macroscopically normal mucosa. Investigation of inflammatory bowel disease, whether for diagnosis or follow-up evaluation, and suspected colonic polyps are the most common indication for pediatric colonoscopy. The child who presents with signs and symptoms of lower gastrointestinal disorder should undergo colonoscopy with biopsy to make the diagnosis, as well as to help determine the appropriate therapy. This review introduces practical information on pediatric colonoscopy, the author's experiences, and the role of colonoscopic examination in the diagnosis and treatment of pediatric lower gastrointestinal disorders.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.6
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• pp.849-855
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• 2008

The Diagnostic Unit of CLINAID can infer working diagnoses for general diseases from the information provided by a user. This user-provided information in the form of signs and symptoms, however, is usually not sufficient to make a final decision on a working diagnosis. In order for the Diagnostic Unit to reach a diagnostic conclusion, it needs to select suitable clinical investigations for the patients. Because different investigations can be selected for the same patient, we need a process that can optimize the selection procedure employed by the Diagnostic Unit. This process, called a selection algorithm, must work with the fuzzy relational method because CLINAID uses fuzzy relational structures extensively for its knowledge bases and inference mechanism. In this paper we present steps of the selection algorithm along with simulation results on this algorithm using fuzzy relational products, both harsh product and mean product. The computation results of applying several different fuzzy implication operators are compared and analyzed.

• Journal of Korea Multimedia Society
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• v.12 no.12
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• pp.1701-1709
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• 2009

The mobile terminal system plays a key role in medical industries which require in fast and accurate diagnosis from heterogeneous acquisition equipment. The demand for PACS (picture archiving and communication systems) has continued to increase in major hospitals and private clinics. Patient care depends on how fast the medical imaging system provides images and how accurately the images are interpreted by physicians. In this paper, we propose an efficient method to decipher the hundreds of images required by physicians to accurately diagnose patients. By exploring Motion- JPEG (M-JPEG), this paper has demonstrates the possibilities for efficient management of medical images with a newly designed image file format and improvement in imaging diagnoses through the replaying of moving pictures of a patient in a mobile environment.

• Journal of information and communication convergence engineering
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• v.17 no.2
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• pp.128-134
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• 2019

Based on the concept of Industry 4.0, various sensors are attached to facilities and equipment to collect data in real time and diagnose faults using analyzing techniques. Diagnostic technology continuously monitors faults or performance degradation of facilities and equipment in operation and diagnoses abnormal symptoms to ensure safety and availability through maintenance before failure occurs. In this paper, we propose a model to analyze the data and diagnose the state or failure using machine learning. The diagnosis model is based on a support vector machine (SVM)-based diagnosis model and a self-learning one-class SVM-based diagnostic model. In the future, it is expected that this model can be applied to facilities used in the entire industry by applying the actual data to the diagnostic model proposed in this paper, conducting the experiment, and verifying it through the model performance evaluation index.

• Journal of Digestive Cancer Research
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• v.7 no.2
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• pp.40-44
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• 2019

Gastric polyps are morphological diagnoses that collectively refer to various types of lesions, and are commonly found by accident in gastroscopy. Gastric polyps in the broad sense are commonly referred to as abnormal structures protruding toward the gastric lumen, but are generally used only in lesions of the gastric mucosa. The incidence of gastric polyps ranged from 1 to 30% and varied by reporter, and differs by region. Fundic gland polyps were the most common in the West, while hyperplastic polyps were the most common in Korea. Gastric polyps are usually discovered by accident, but because some kinds of polyps have the potential to transform of malignant tumors, histological diagnosis is essential. There should be adequate treatment and management according to the histological results.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.971-980
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• 2023

Safety accidents and industrial accidents are constantly occurring in existing industrial sites. In addition, the probability of accidents occurring due to physical and mental fatigue of workers is increasing. Accordingly, it is required to introduce systematic management and various systems for the safety of workers. In this paper, by developing an access control system using bio-metric information at industrial sites, we develop efficient health management and access control management functions for workers. Workers are identified through face recognition for access control, and health status is determined through iris recognition. It aims to improve accuracy and develop a more efficient management system by diagnosing signs of health abnormalities through the congestion of the iris and eyes of workers. Finally, the contents of the development consist of an on-site access control system, an access control program for administrators, and a main server system that diagnoses signs of abnormal health of users.

• Health Policy and Management
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• v.14 no.1
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• pp.121-147
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• 2004

Since the pilot program for a DRG-based prospective payment system was introduced in 1997, the performance of KDRGs has been one of hotly debated issues. The objectives of this study are to refine the classification algorithm of the KDRGs and to assess the improvement achieved by the refinement. The U.S. Medicare DRGs version 17.0 and the Australian Refined DRGs version 4.1 were reviewed to identify areas of possible impro-vement. Refined changes in the classification and result of date analyses were submitted to a panel of 48 physicians for their reviews and suggestions. The refinement was evaluated by the variance reduction in resource utilization achieved by the KDRG The database of 2,182,168 claims submitted to the Health Insurance Review Agency during 2002 was used for evaluation. As the result of the refinement, three new MDCs were introduced and the number of ADEGs increased from 332 to 674. Various age splits and two to four levels of severity classification for secondary diagnoses were introduced as well. A total of 1,817 groups were defined in the refined KDRGs. The variance reduction for charges of all patients increased from 48.2％ to 53.6％ by the refinement, and from 65.6％ to 73.1％ for non-outlier patients. The r-square for length of stays of all patients was increased from 28.3％ to 32.6％, and from 40.4％ to 44.9％ for non-outlier patients. These results indicated a significant improvement in the classification accuracy of the KDRG system.

• Knowledge Management Research
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• v.15 no.3
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• pp.121-140
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• 2014

Many firms have been utilizing SNSs such as Facebook and Twitter actively in order to boost interactions with customers that promote product and service innovations and effective marketing. Although positive outcomes of the customer interactions in SNSs are expected, there exist few studies on the effects of interactions between customers and firms in the SNS context. This study empirically examines how customer experiences in multi-dimensional interactions (i.e., pragmatic, sociability, usability, and hedonic interaction) in corporate SNSs influence customer trust in the firm, and how customer learning benefits are associated with firm benefits such as gaining customer trust. The results indicate that all four dimensions of customer interactions in SNSs have significant effects on customer learning benefits, which in turn significantly influence customer trust in the firm. Meanwhile, the results reveal that there are also direct relationships between specific dimensions of customer interactions in SNSs and the two dimensions of customer trust (i.e., ability-based and benevolence/integrity-based). Based on the findings, this study diagnoses the status of corporate SNSs in terms of collaboration with customers and provides practical implications for firms which attempt to capitalize on the multi-dimensional customer interactions in SNSs and to facilitate innovative activities with customers.

• Journal of Medicine and Life Science
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• v.20 no.4
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• pp.141-157
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• 2023

Stroke is a leading cause of disability and death. The condition requires prompt diagnosis and treatment. The quality of care provided to patients with stroke can vary depending on the availability of medical resources, which in turn, can affect prognosis. Recently, there has been growing interest in using machine learning (ML) to support stroke diagnosis and treatment decisions based on large medical data sets. Current ML applications in stroke care can be divided into two categories: analysis of neuroimaging data and clinical information-based predictive models. Using ML to analyze neuroimaging data can increase the efficiency and accuracy of diagnoses. Commercial software that uses ML algorithms is already being used in the medical field. Additionally, the accuracy of predictive ML models is improving with the integration of radiomics and clinical data. is expected to be important for improving the quality of care for patients with stroke.