• Women's Health Nursing
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• v.22 no.3
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• pp.170-181
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• 2016

Purpose: The aim of this study was to identify the nursing diagnosis-outcome-intervention (NANDA-NOC-NIC) linkages for gynecology inpatients shown in their electronic nursing records. Methods: This retrospective and descriptive research was conducted in two steps and based on the 287 electronic nursing records for 253 patients. First, nursing diagnoses, outcomes and interventions were collected. To identify major nursing diagnoses, a comparison was done with the top 10 nursing diagnoses from this research and with previous research selected using a content validity index developed by a team of professionals. Second, nursing outcomes and interventions that were associated with major nursing diagnoses were identified. Results: Nineteen nursing diagnoses, 12 nursing outcomes, and 40 nursing interventions were collected. The top 5 major nursing diagnoses were identified and 7 nursing outcomes and 18 nursing interventions associated with these diagnoses were checked. Conclusion: The identified NANDA-NOC-NIC linkages can contribute to improving nursing practice and will help in the establishment of standardized nursing care.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.26 no.8
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• pp.351-356
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• 2014

Typical trouble patterns in solar thermal systems include working fluid leakage and freezing other than breakdown of pump. A fluid sensor for measuring electric resistance of fluid was developed and installed at the top of the collector piping in order to check the fault of solar system. Working fluid level in the pipe was determined by measuring electric resistance from a fluid sensor. On the base of this, it was confirmed that the fluid sensor diagnoses leakage of fluid. Electric resistance of propylene glycol aqueous solution was measured in the range of $0{\sim}70^{\circ}C$ and 0~40% of concentration. The response surface analysis was performed by using a central composite design, and the regression equation was derived from the relationship between electric resistance, temperature, and concentration. Through the experiment in a real solar system, we can estimate a concentration of working fluid when a pump is not operating and predict a possibility of freezing. Finally, an effective algorithm for trouble shooting was proposed to operate and maintain the solar system.

• Korean Journal of Agricultural Science
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• v.48 no.3
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• pp.483-492
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• 2021

This study attempted to estimate the degree of management of oak mushroom farms in Chungcheongnam-do and to provide information to establish oak mushroom cultivation-related policies. The oak mushroom management standard diagnostic table consists of three major categories, growing condition, inoculation management, cultivation management and management administration, along with 20 subcategories. Thus, 209 households of oak mushroom farms were surveyed from 2015 to 2018 in Gongju, Cheongyang, Buyeo and Seochun in Chungcheongnam-do. The average score for the 20 subcategories was 71.5 points (representing a significant level), indicating that these areas have excellent management conditions. The analysis of the management performance indicators revealed a high number of indicators with scores of five or above. The total score was higher, and the amount per bed log and the rate of top-grade products in the total output were also higher, indicating a significant correlation. These findings will provide consulting services to oak mushroom growers as they highlight the correlation between the higher scores of indicators in the oak mushroom management standard diagnostic table and the management performance of farmers. We found that the scores of the indicators for management administration, such as management record and analysis and fund plan were relatively lower than those of other indicators. It is assumed that the owners aging has led to negligence in recording the details on incomes, expenditures, and work and lowered the willingness to make substantial profits. Therefore, it is essential to overcome these problems for profitable oak mushroom farming.

• Journal of Conservation Science
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• v.38 no.4
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• pp.334-346
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• 2022

The manufacturing technique was studied through the structure and material characteristics of the walls and the painting layers of the Avalokitesvara Bodhisattva mural of Geungnakjeon Hall, Daewonsa Temple. The mural is painted and connected to the earthen wall and the Junggit, and the wall is composed of wooden laths as a frame, the first and middle layers, the finishing layer, and the painting layer. The first layer, middle layer, and finishing layer constituting the wall were made by mixing weathered soil and sand. It was confirmed that the first layer had a high content of loess below silt, and the finishing layer had a high content of fine-sand and very fine sand. For the painting layer, a ground layer was prepared using soil-based mineral pigments, and lead white, white clay, atacamite, minium, and cinnabar (or vermilion) pigments were used on top of it. The Avalokitesvara Bodhisattva mural was confirmed to belong to a category similar to the soil-made buddhist mural paintings of Joseon Dynasty. However, it shows characteristics such as a high content of fine sand in the finishing layer and overlapping over other colors. Such material and structural characteristics can constitute important information for future mural conservation status diagnoses and conservation treatment plans.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.399-400
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• 2009

As a part of the era of human centric applications people started to care about their well being utilizing any possible mean. This paper proposes a framework for real time on-body sensor health-care system, addresses the current issues in such systems, and utilizes an enhanced online divisive agglomerative clustering algorithm (EODAC); an algorithm that builds a top-down tree-like structure of clusters that evolves with streaming data to rationally cluster on-body sensor data and give accurate diagnoses remotely, guaranteeing high performance, and scalability. Furthermore it does not depend on the number of data points.

• Quality Improvement in Health Care
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• v.12 no.2
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• pp.84-92
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• 2006

Background : To examine the difference between ICD-10 and The Korean standard classification of disease(oriental medicine), and to aim at improve the practical use as statistical data. It is one of the reason of disease classification. On that account we convert the many to many correspondence presenting classification of oriental medicine into many to one correspondence. Method : The study tracked out 155 patients discharged from the university hospital which is located in Gyeonggi Province and managing hospital and oriental medicine hospital from July to October this year. The period of this study was from August 1 to November 18. We compared correspondence between the two services' diagnosis(hospital services and oriental medicine hospital services) at the same time and attempted many to one correspondence classification. That is for production of statistical data. Result : We investigated the group which have had medical treatment experience of two kinds of services at the same time. The result of this investigation was that the same oriental medicine diagnosis used differently in western medicine diagnosis. 44.5% was accorded with western medicine diagnosis. Correspondence of the western medicine diagnose with the top of the Korean standard classification of disease(oriental medicine) list's western medicine diagnosis was 13.5%. For many to one correspondence classification for statistics, one western medicine diagnosis was selected for one oriental medicine diagnosis. In case of the main diagnosis(I sign) was not enough to explain oriental medicine diagnosis' characteristic, we chose multiple other diagnosis, so other diagnosis(II sign) about patient's cause of disease could be selected for supplement after we examined the patient's records. The statistics was possible with this many to one correspondence. Conclusion : The result of this study about correspondence between western medicine diagnoses and those of oriental medicine confirms that The Korean standard classification of disease(oriental medicine) is hard to be standardized with western medicine diagnosis. Therefore, according to this study, we use new many to one correspondence classification, multiple oriental medicine diagnoses with one ICD-10, which can be used by statistical data.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.239-253
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• 2024

This research applies both point-wise and pair-wise learning strategies within the learning-to-rank (LTR) framework to predict horse race rankings in Seoul. Specifically, for point-wise learning, we employ a linear model and random forest. In contrast, for pair-wise learning, we utilize tools such as RankNet, and LambdaMART (XGBoost Ranker, LightGBM Ranker, and CatBoost Ranker). Furthermore, to enhance predictions, race records are standardized based on race distance, and we integrate various datasets, including race information, jockey information, horse training records, and trainer information. Our results empirically demonstrate that pair-wise learning approaches that can reflect the order information between items generally outperform point-wise learning approaches. Notably, CatBoost Ranker is the top performer. Through Shapley value analysis, we identified that the important variables for CatBoost Ranker include the performance of a horse, its previous race records, the count of its starting trainings, the total number of starting trainings, and the instances of disease diagnoses for the horse.

• Quality Improvement in Health Care
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• v.2 no.2
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• pp.56-71
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• 1996

Background : Review of readmissions in health care facilities is necessary from the viewpoint of both economic concerns and quality considerations. To identify the characteristics, factors, and causes of multiple admissions in comparison with single admissions is essential for both providers and payers in order to assure quality care and efficient use of medical resources. Methods: All discharges from an university hospital in 1993 were analyzed, and the characteristics of multiple admissions were identified and were compared with those of single admissions by using the data bases of the discharge abstract and billing for reimbursement. Medical records of patients readmitted within 6 days after the previous discharge were reviewed to identify the reasons for such prompt readmission. Statistical analysis between groups of patients were performed by using SPSS. Result : The mean age was higher in multiple admissions than those of single admissions, and the average length of stay was longer in multiple admissions than in single admissions. The hospital cost per day is higher in single admissions while the cost per case is higher in multiple admissions. More than half of readmissions occurred within one month after the preceding discharges. Above 15% of the readmission within 6 days after the preceding discharges seemed to have close relationship with quality of care provided during the preceding hospitalization. The death rate of the patients readmitted within 6 days was the highest in comparison with multiple admissions and single admissions. Conclusion : Potential preventable readmissions should be reduced by identifying characteristics of multiple admissions, especially unplanned readmission, and by applying some interventions such as standard predischarge assessment or careful follow-up care after discharge for high risk readmission groups. As the results of these efforts, health care facilities could achieve quality improvement in medical care, and effective use of hospital resources.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.7-14
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• 2021

Tomato crops are highly affected by tomato diseases, and if not prevented, a disease can cause severe losses for the agricultural economy. Therefore, there is a need for a system that quickly and accurately diagnoses various tomato diseases. In this paper, we propose a system that classifies nine diseases as well as healthy tomato plants by applying various pretrained deep learning-based CNN models trained on an ImageNet dataset. The tomato leaf image dataset obtained from PlantVillage is provided as input to ResNet, Xception, and DenseNet, which have deep learning-based CNN architectures. The proposed models were constructed by adding a top-level classifier to the basic CNN model, and they were trained by applying a 5-fold cross-validation strategy. All three of the proposed models were trained in two stages: transfer learning (which freezes the layers of the basic CNN model and then trains only the top-level classifiers), and fine-tuned learning (which sets the learning rate to a very small number and trains after unfreezing basic CNN layers). SGD, RMSprop, and Adam were applied as optimization algorithms. The experimental results show that the DenseNet CNN model to which the RMSprop algorithm was applied output the best results, with 98.63% accuracy.