• Journal of Oriental Neuropsychiatry
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• v.34 no.2
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• pp.95-113
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• 2023

Objectives: To review studies evaluating effects of acupuncture on pain and depressive symptoms in fibromyalgia. Methods: Quantitative evidences (RCTs) were systematically reviewed. Literature were searched for a combination of fibromyalgia and depression (The Cochrane Central Register of Controlled Trials (CENTRAL), EMBASE, medline (via PubMed), Kmbase, KISS, ScienceON, OASIS, CiNii, CNKI). Quantitative research findings were critically appraised by Cochrane risk of bias (RoB) tool and pooled. Meta-analysis was then conducted using Review Manager (RevMan) 5.4. Results: Eighteen studies were selected. American College of Rheumatology (ACR) classification criteria for Fibromyalgia Syndrome was most frequently used as diagnostic criteria for fibromyalgia. As for outcome measurement, Hamilton Rating Scale for Depression (HAMD), Visual Analog Scale (VAS), and Total Effective Rate (TER) were used most commonly. Meta-analysis of ten studies revealed that both Depression and VAS scores of the Acupuncture+Western Medicine group were significantly lower than those of Western Medicine group (Depression: SMD, -0.94, 95% CI, -1.17 to -0.70; VAS: MD, -1.51, 95% CI, -1.83 to -1.19). Also, TERs of both Acupuncture group and Acupuncture+Western Acupuncture+Western Medicine group were significantly higher than those of the Western Medicine group (OR: 2.38, 95% CI: 1.29 to 4.41; and OR: 7.40, 95% CI: 3.41 to 16.07). There was no significant difference in Depression or VAS score between the Acupuncture Group and the Western Medicine Group. Conclusions: Acupuncture might be an effective option for pain and depressive symptoms of fibromyalgia when it is combined with Western Medicine treatment. For more accurate results, more types of Korean medicine treatment should be conducted.

• Clinics in Shoulder and Elbow
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• v.13 no.1
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• pp.20-26
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• 2010

Purpose: To evaluate clinical features and surgical results for rotator cuff tear secondary to shoulder dislocation in middle-aged and elderly patients. Materials and Methods: We reviewed 19 patients over 50 years of age who had rotator cuff tears combined with shoulder dislocation between October 2004 and October 2008. There were 7 males and 12 females with a mean age 64.7 years (range, 50 to 78 years). The average follow-up duration was 22 months (range, 8 to 56 months). We investigated the number of dislocations, the size of the cuff tear, the presence of Bankart lesions and the time interval from dislocation to surgery. We also investigated the ASES score, UCLA score, SST score, and shoulder range of motion before and after surgery. We analyzed clinical outcomes and contributing factors. Results: ASES scores improved from 30.2 preoperatively to 72.3 postoperatively; UCLA scores improved from 12.9 to 26.5; SST scores improved from 2.4 to 7.3. Range of motion improved significantly: forward flexion, abduction, external rotation and internal rotation were, respectively, $110.8({\pm}39.3)^{\circ}$, $107.7({\pm}40)^{\circ}$, $22.5({\pm}17.6)^{\circ}$ and L5 level preoperatively; postoperatively they were $153.6({\pm}20.6)^{\circ}$, $152.1({\pm}20.8)^{\circ}$, $36.4({\pm}22.7)^{\circ}$ and L1 level. Age, the presence of Bankart lesions and the number of dislocations were not correlated with clinical outcomes. But the size of the cuff tear was correlated with clinical results. Also, the duration from dislocation to surgery was correlated with postoperative UCLA and SST scores (p=0.039, p=0.038). Conclusion: For shoulder dislocation, it is important to achieve early diagnoses of rotator cuff tears in middle-aged and elderly patients. If these injuries are both present, early rotator cuff repair should be performed for better clinical results.

• Journal of Yeungnam Medical Science
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• v.9 no.2
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• pp.275-287
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• 1992

In order to survey the reality of anxiety and depression among the Koreans residing in China, a study was conducted between January and March of 1991, on the residents of Yun-Kil city, with subjects of 472 Koreans and 479 Chinese. The evaluation was based on the questionairs, named Combined self-rating anxiety depression scale(CADS), distributed among the subjects. ANOVA and t-test were applied for data processing. The results were as follows : There was not significant difference in the mean of total scores between the two groups. The scores of Koreans were $29.70{\pm}7.03$, while those of Chinese were $29.45{\pm}9.01$. The score of the CADS above 50(clinially significant level) was seen in 12(2.54%) Koreans and 21(4.38%) Chinese. The anxiety-depression scores relating to the items of indigestion and decreased appetite, sleep disturbance, apprehension, decreased libido were relatively high among the Koreans. The items appeared low in scores among the Koreans were faintness, fear, suicidal rumination, hopelessness, paresthesias. The highs among the Chinese were facial flushing, anxiousness, dissatisfaction, suicidal rumination. The items appeared low among the Chinese were fear, faintness, paresthesias, weight loss, suicidal rumination. In the comparison of evaluation by items between the two groups, the items placing the Koreans significantly higher over the Chinese are indigestion & decreased appetite, sleep disturbance, apprehension, decreased libido. The Chinese marked significantly higher in facial flushing, anxiousness, dissatisfaction, suicidal rumination. Those in the case of female (p<0.01 respectively), less than twenty years old (p<0.01 respectively), dissatisfied with family relationship(p<0.01 respectively), with past history of psychiatric hospitalization(Koreans p<0.01, Chinese p<0.05), pessimistic toward future, present, past self image(p<0.01 respectively) had significantly higher scores in both groups. In religion, neither group showed significant difference. In religion, neither group showed significant difference. In marital status, the Koreans showed a higher degree of divorce and separation and the Chinese in singleness(p<0.01 respectively). The Korean were higher in illiteracy and the Chinese had more college education(p<0.01 respectively). In place of growth, the Koreans showed not much difference in the areas while more Chinese grew up un large cities(p<0.01). More Koreans lived in the dormitory while the Chinese were engaged more in self-cooking(p<0.01 respectively). In pocket money per mouth, more Koreans were less than 1 dollar while the Chinese were between 7 and 10 dollars(p<0.01 respectively). There were no significant difference between two groups about religion.

• Journal of the Korean Society for Library and Information Science
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• v.12
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• pp.269-327
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• 1985

This study was undertaken to meet more fully the demands for improved training of library personnel, occasioned by the rapidly changing roles and functions of libraries as they try to adapt to the vast social, economic and technological changes currently in progress in the Korean society. The specific purpose of this research is to develop a standard curriculum at the batchelor's level that will properly equip the professional personnel in Korean libraries for the changes confronting them. This study started with the premise that to establish a sound base for curriculum development, it was necessary first to determine what concepts, knowledge, and techniques are required for professional library personnel to perform it at an optimal level of efficiency. Explicitly, it was felt that for the development of useful curricula and courses at the batchelor's level, a prime source of knowledge should be functional behaviours that are necessary in the job situation. To determine specifically what these terminal performance behaviours should be so that learning experience provided could be rooted in reality, the decision was reached to use a systems approach to curriculum development, which is an attempt to break the mold of traditional concepts and to approach interaction from an open, innovative, and product-oriented perspective. This study was designed to: (1) identify what knowledge and techniques are required for professional library personnel to perform the job activities in which they are actually engaged, (2) to evaluate the educational needs of the knowledge and techniques that the professional librarian respondents indicate, and (3) to categorise the knowledge and techniques into teaching subjects to present the teaching subjects by their educational importance. The main data-gathering instrument for the study, a questionnaire containing 254 items, was sent to a randomly selected sample of library school graduates working in libraries and related institutions in Korea. Eighty-three librarians completed and returned the questionnaire. After analysing the returned questionnaire, the following conclusions have been reached: (A) To develop a rational curriculum rooted in the real situation of the Korean libraries, compulsory subjects should be properly chosen from those which were ranked highest in importance by the respondents. Characters and educational policies of, and other teaching subjects offered by, the individual educational institution to which a given library school belongs should also be taken into account in determining compulsory subjects. (B) It is traditionally assumed that education in librarianship should be more concerned with theoretical foundations on which any solution can be developed than with professional needs with particulars and techniques as they are used in existing library environments. However, the respondents gave the former a surprisingly lower rating. The traditional assumption must be reviewed. (C) It is universally accepted in developing library school curricula that compulsory subjects are concerned with the area of knowledge students generally need to learn and optional subjects are concerned with the area to be needed to only those who need it. Now that there is no such clear demarcation line provided in librarianship, it may be a realistic approach to designate subjects in the area rated high by the respondents as compulsory and to designate those in the area rated low as optional. (D) Optional subjects that were ranked considerably higher in importance by the respondents should be given more credits than others, and those ranked lower might be given less credits or offered infrequently or combined. (E) A standard list of compulsory and optional subjects with weekly teaching hours for a Korean library school is presented in the fourth chapter of this report.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.157-173
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• 2021

With the development of information technology, the amount of available information increases daily. However, having access to so much information makes it difficult for users to easily find the information they seek. Users want a visualized system that reduces information retrieval and learning time, saving them from personally reading and judging all available information. As a result, recommendation systems are an increasingly important technologies that are essential to the business. Collaborative filtering is used in various fields with excellent performance because recommendations are made based on similar user interests and preferences. However, limitations do exist. Sparsity occurs when user-item preference information is insufficient, and is the main limitation of collaborative filtering. The evaluation value of the user item matrix may be distorted by the data depending on the popularity of the product, or there may be new users who have not yet evaluated the value. The lack of historical data to identify consumer preferences is referred to as data sparsity, and various methods have been studied to address these problems. However, most attempts to solve the sparsity problem are not optimal because they can only be applied when additional data such as users' personal information, social networks, or characteristics of items are included. Another problem is that real-world score data are mostly biased to high scores, resulting in severe imbalances. One cause of this imbalance distribution is the purchasing bias, in which only users with high product ratings purchase products, so those with low ratings are less likely to purchase products and thus do not leave negative product reviews. Due to these characteristics, unlike most users' actual preferences, reviews by users who purchase products are more likely to be positive. Therefore, the actual rating data is over-learned in many classes with high incidence due to its biased characteristics, distorting the market. Applying collaborative filtering to these imbalanced data leads to poor recommendation performance due to excessive learning of biased classes. Traditional oversampling techniques to address this problem are likely to cause overfitting because they repeat the same data, which acts as noise in learning, reducing recommendation performance. In addition, pre-processing methods for most existing data imbalance problems are designed and used for binary classes. Binary class imbalance techniques are difficult to apply to multi-class problems because they cannot model multi-class problems, such as objects at cross-class boundaries or objects overlapping multiple classes. To solve this problem, research has been conducted to convert and apply multi-class problems to binary class problems. However, simplification of multi-class problems can cause potential classification errors when combined with the results of classifiers learned from other sub-problems, resulting in loss of important information about relationships beyond the selected items. Therefore, it is necessary to develop more effective methods to address multi-class imbalance problems. We propose a collaborative filtering model using CGAN to generate realistic virtual data to populate the empty user-item matrix. Conditional vector y identify distributions for minority classes and generate data reflecting their characteristics. Collaborative filtering then maximizes the performance of the recommendation system via hyperparameter tuning. This process should improve the accuracy of the model by addressing the sparsity problem of collaborative filtering implementations while mitigating data imbalances arising from real data. Our model has superior recommendation performance over existing oversampling techniques and existing real-world data with data sparsity. SMOTE, Borderline SMOTE, SVM-SMOTE, ADASYN, and GAN were used as comparative models and we demonstrate the highest prediction accuracy on the RMSE and MAE evaluation scales. Through this study, oversampling based on deep learning will be able to further refine the performance of recommendation systems using actual data and be used to build business recommendation systems.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.173-198
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• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.