• Journal of the Korean Orthopaedic Association
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• v.56 no.1
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• pp.26-33
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• 2021

Purpose: This study evaluated the results of dual plate fixation for periprosthetic femur fracture after total knee arthroplasty (TKA). Materials and Methods: From October 2007 to February 2013, 23 cases of periprosthetic femur fracture after TKA were treated at the author's hospital. There were 13 cases of fixation using a medial and lateral dual plate when the stability of the fracture site could not be achieved by one side fixation with a follow-up of more than one year. The cases included no loosening of the femoral component in fractures that were categorized as Lewis-Rorabeck classification II and supracondylar comminuted fractures and elongation of the fracture line to the lateral epicondyle of the femur or stem in the medullary canal. The mean age was 72 years (65-82 years), and 11 cases were female. Three cases had a stem due to revision. The mean bone marrow density was -3.2 (-1.7 to -4.4), and the mean period from primary TKA to periprosthetic fractures was 28 months (1-108 months). The mean follow-up period was 23 months (12-65 months). The medial fracture site was first exposed via the subvastus approach. Second, the supplementary plate was fixed on the lateral side of the fracture using a minimally invasive plate osteosynthesis technique. The average union time, complications, and Hospital for Special Surgery Knee Score (HSS) at the last follow-up were evaluated. Results: The mean union time was 17.4 weeks (7-40 weeks). Two cases showed delayed bone union and nonunion occurred in one case, in whom bone union was achieved three months later after re-fixation using a dual plate with an autogenous bone graft. The mean varusvalgus angulation was 1.67 degrees (-1.2-4.9 degrees), and the mean anterior-posterior angulation was 2.86 degrees (0-4.9 degrees) at the last follow-up. The mean knee range of motion was 90 degrees, and the HSS score was 85 points (70-95 points) at the last follow-up. Conclusion: Dual plate fixation for periprosthetic femur fractures that had not achieved stability by one side plate fixation after TKA showed a good clinical result that allowed early rehabilitation.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.97-113
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• 2016

Due to the growth of internet data and the rapid development of internet technology, "big data" analysis is actively conducted to analyze enormous data for various purposes. Especially in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of existing structured data analysis. Various studies on sentiment analysis, the part of text mining techniques, are actively studied to score opinions based on the distribution of polarity of words in documents. Usually, the sentiment analysis uses sentiment dictionary contains positivity and negativity of vocabularies. As a part of such studies, this study tries to construct sentiment dictionary which is customized to specific data domain. Using a common sentiment dictionary for sentiment analysis without considering data domain characteristic cannot reflect contextual expression only used in the specific data domain. So, we can expect using a modified sentiment dictionary customized to data domain can lead the improvement of sentiment analysis efficiency. Therefore, this study aims to suggest a way to construct customized dictionary to reflect characteristics of data domain. Especially, in this study, movie review data are divided by genre and construct genre-customized dictionaries. The performance of customized dictionary in sentiment analysis is compared with a common sentiment dictionary. In this study, IMDb data are chosen as the subject of analysis, and movie reviews are categorized by genre. Six genres in IMDb, 'action', 'animation', 'comedy', 'drama', 'horror', and 'sci-fi' are selected. Five highest ranking movies and five lowest ranking movies per genre are selected as training data set and two years' movie data from 2012 September 2012 to June 2014 are collected as test data set. Using SO-PMI (Semantic Orientation from Point-wise Mutual Information) technique, we build customized sentiment dictionary per genre and compare prediction accuracy on review rating. As a result of the analysis, the prediction using customized dictionaries improves prediction accuracy. The performance improvement is 2.82% in overall and is statistical significant. Especially, the customized dictionary on 'sci-fi' leads the highest accuracy improvement among six genres. Even though this study shows the usefulness of customized dictionaries in sentiment analysis, further studies are required to generalize the results. In this study, we only consider adjectives as additional terms in customized sentiment dictionary. Other part of text such as verb and adverb can be considered to improve sentiment analysis performance. Also, we need to apply customized sentiment dictionary to other domain such as product reviews.

• Journal of Korean Foot and Ankle Society
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• v.1 no.2
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• pp.102-111
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• 1997

The tibial Pilon fracture, which is defined as a comminuted intraarticular fracture of the distal tibia, is difficult to manage because high axial compression and rotational forces to the ankle joint result in impaction, severe comminution, metaphyseal disruption and soft tissue damage. There are variable methods of treatment such as manipulation and cast, calcaneal traction and cast, external fixation, pin and plaster, limited open reduction and external fixation, and open reduction and rigid internal fixation. Though most of authors reported better result after a surgical treatment. than that of conservative treatment, many complications such as posttraumatic arthritis and soft tissue problem still remain troublesome. We have reviewed 19 cases of the tibial Pilon fractures in 18 patients which were treated with limited open reduction and internal fixation from September 1993 to May 1996. The results were as follows: 1. The fractures were classified into five types according to the system of Ovadia and Beals, and the most frequent type was type 3 (53%). The most common cause of injury was traffic accident (47%). 2. All of the cases of type 1 and 2, in which the injury of the ankle joint was less severe, revealed good or excellent clinical results. But in type 4 and 5, because the injury is much severe and accurate reduction is difficult, the clinical results were unsatisfaetory. 3. The most frequent complication was posttraumatic osteoarthritis, and which developed in second frequent complication, was developed m the three cases of type 3 in which the radiographic results were less than fair, but there were no correlation with the clinical results. 4. We could markedly reduce the complications related to the soft tissue problem of Pilon fracture by treatment with limited open reduction and internal fixation, and consider that this is a good method of treatment of Pilon fracture when the injury is less severe and accurate reduction is possible.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.113-125
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• 2013

Every company wants to know customer's requirement and makes an effort to meet them. Cause that, communication between customer and company became core competition of business and that important is increasing continuously. There are several strategies to find customer's needs, but VOC (Voice of customer) is one of most powerful communication tools and VOC gathering by several channels as telephone, post, e-mail, website and so on is so meaningful. So, almost company is gathering VOC and operating VOC system. VOC is important not only to business organization but also public organization such as government, education institute, and medical center that should drive up public service quality and customer satisfaction. Accordingly, they make a VOC gathering and analyzing System and then use for making a new product and service, and upgrade. In recent years, innovations in internet and ICT have made diverse channels such as SNS, mobile, website and call-center to collect VOC data. Although a lot of VOC data is collected through diverse channel, the proper utilization is still difficult. It is because the VOC data is made of very emotional contents by voice or text of informal style and the volume of the VOC data are so big. These unstructured big data make a difficult to store and analyze for use by human. So that, the organization need to automatic collecting, storing, classifying and analyzing system for unstructured big VOC data. This study propose an intelligent VOC analyzing system based on opinion mining to classify the unstructured VOC data automatically and determine the polarity as well as the type of VOC. And then, the basis of the VOC opinion analyzing system, called domain-oriented sentiment dictionary is created and corresponding stages are presented in detail. The experiment is conducted with 4,300 VOC data collected from a medical website to measure the effectiveness of the proposed system and utilized them to develop the sensitive data dictionary by determining the special sentiment vocabulary and their polarity value in a medical domain. Through the experiment, it comes out that positive terms such as "칭찬, 친절함, 감사, 무사히, 잘해, 감동, 미소" have high positive opinion value, and negative terms such as "퉁명, 뭡니까, 말하더군요, 무시하는" have strong negative opinion. These terms are in general use and the experiment result seems to be a high probability of opinion polarity. Furthermore, the accuracy of proposed VOC classification model has been compared and the highest classification accuracy of 77.8% is conformed at threshold with -0.50 of opinion classification of VOC. Through the proposed intelligent VOC analyzing system, the real time opinion classification and response priority of VOC can be predicted. Ultimately the positive effectiveness is expected to catch the customer complains at early stage and deal with it quickly with the lower number of staff to operate the VOC system. It can be made available human resource and time of customer service part. Above all, this study is new try to automatic analyzing the unstructured VOC data using opinion mining, and shows that the system could be used as variable to classify the positive or negative polarity of VOC opinion. It is expected to suggest practical framework of the VOC analysis to diverse use and the model can be used as real VOC analyzing system if it is implemented as system. Despite experiment results and expectation, this study has several limits. First of all, the sample data is only collected from a hospital web-site. It means that the sentimental dictionary made by sample data can be lean too much towards on that hospital and web-site. Therefore, next research has to take several channels such as call-center and SNS, and other domain like government, financial company, and education institute.

• Journal of the Korean Arthroscopy Society
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• v.10 no.1
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• pp.53-60
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• 2006

Purpose: This study is to analyze the clinical and radiological results after arthroscopic reduction and internal fixation of intra-articular fractures of lateral tibial plateau. Materials and Methods: The subject of the study are the 13 cases of the patients visited orthopedics surgery during March year 2000 to August year 2004 because of intra-articular fractures of lateral tibial plateau and were treated with arthroscopic reduction and internal fixation. X-rays and CT or MRI were both carried out to identify the precise pattern of fracture and the degree of depression which showed out to be all type 2 by Schatzker fracture classification. And in 9 of the cases, autogenous and allogenous bone grafts were given as bone loss were severe. The average age was 48, age group between 31 and 66, and average follow up period of about 38 months ($13{\sim}65months$). Radiological ratings were given by comparing the X-rays of degree of joint congruency before and after the operation, functional ratings by analyzing IKDC score and Lysholm score. Combined injuries observed after arthroscopy were posterior cruciate ligament injury in 1 case, meniscus injury in 4 cases and medial collateral ligament in 2 cases. Results: During follow up, X-rays showed well-maintained reduction of articular surface in all cases and no complications such as joint depression, fracture reduction loss, angular deformity or malunion were found. Average Lysholm score at last follow up was 87 points ranging from 65 to 97, in 8 of the cases excellent, 3 good, 1 fair and 1 poor according to Lynsholm classification. Average IKDC score was 92 (from 82 to 99). Conclusion: Not only does arthroscopic reduction of lateral tibial plateau fracture bring exact reduction of articular surface, but also, is considered to be a good way of operation to diagnose and treat combined injuries of knee joint using arthroscopy.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.49-67
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• 2015

Recently, emerging the notion of big data and social media has led us to enter data's big bang. Social networking services are widely used by people around the world, and they have become a part of major communication tools for all ages. Over the last decade, as online social networking sites become increasingly popular, companies tend to focus on advanced social media analysis for their marketing strategies. In addition to social media analysis, companies are mainly concerned about propagating of negative opinions on social networking sites such as Facebook and Twitter, as well as e-commerce sites. The effect of online word of mouth (WOM) such as product rating, product review, and product recommendations is very influential, and negative opinions have significant impact on product sales. This trend has increased researchers' attention to a natural language processing, such as a sentiment analysis. A sentiment analysis, also refers to as an opinion mining, is a process of identifying the polarity of subjective information and has been applied to various research and practical fields. However, there are obstacles lies when Korean language (Hangul) is used in a natural language processing because it is an agglutinative language with rich morphology pose problems. Therefore, there is a lack of Korean natural language processing resources such as a sentiment lexicon, and this has resulted in significant limitations for researchers and practitioners who are considering sentiment analysis. Our study builds a Korean sentiment lexicon with collective intelligence, and provides API (Application Programming Interface) service to open and share a sentiment lexicon data with the public (www.openhangul.com). For the pre-processing, we have created a Korean lexicon database with over 517,178 words and classified them into sentiment and non-sentiment words. In order to classify them, we first identified stop words which often quite likely to play a negative role in sentiment analysis and excluded them from our sentiment scoring. In general, sentiment words are nouns, adjectives, verbs, adverbs as they have sentimental expressions such as positive, neutral, and negative. On the other hands, non-sentiment words are interjection, determiner, numeral, postposition, etc. as they generally have no sentimental expressions. To build a reliable sentiment lexicon, we have adopted a concept of collective intelligence as a model for crowdsourcing. In addition, a concept of folksonomy has been implemented in the process of taxonomy to help collective intelligence. In order to make up for an inherent weakness of folksonomy, we have adopted a majority rule by building a voting system. Participants, as voters were offered three voting options to choose from positivity, negativity, and neutrality, and the voting have been conducted on one of the largest social networking sites for college students in Korea. More than 35,000 votes have been made by college students in Korea, and we keep this voting system open by maintaining the project as a perpetual study. Besides, any change in the sentiment score of words can be an important observation because it enables us to keep track of temporal changes in Korean language as a natural language. Lastly, our study offers a RESTful, JSON based API service through a web platform to make easier support for users such as researchers, companies, and developers. Finally, our study makes important contributions to both research and practice. In terms of research, our Korean sentiment lexicon plays an important role as a resource for Korean natural language processing. In terms of practice, practitioners such as managers and marketers can implement sentiment analysis effectively by using Korean sentiment lexicon we built. Moreover, our study sheds new light on the value of folksonomy by combining collective intelligence, and we also expect to give a new direction and a new start to the development of Korean natural language processing.