• Tuberculosis and Respiratory Diseases
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• v.45 no.1
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• pp.5-19
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• 1998

Introduction: Direct histologic and bacteriologic examination of a representative specimen of lung tissue is the only certain method of providing an accurate diagnosis in various pulmonary diseases including diffuse pulmonary diseases. The purpose of national survey was to define the indication, incidence, effectiveness, safety and complication of open and thoracoscopic lung biopsy in korea. Methods: A multicenter registry of 37 university or general hospitals equipped more than 400 patient's bed were retrospectively collected and analyzed for 3 years from the January 1994 to December 1996 using the same registry protocol. Results: 1) There were 511 cases from the 37 hospitals during 3 years. The mean age was 50.2 years(${\pm}15.1$ years) and men was more prevalent than women(54.9% vs 45.9%). 2) The open lung biopsy was performed in 313 cases(62%) and thoracoscopic lung biopsy was performed in 192 cases(38%). The incidence of lung biopsy was more higher in diffuse lung disease(305 cases, 59.7%) than in localized lung disease(206 cases, 40.3%) 3) The duration after abnormalities was found in chest X-ray until lung biopsy was 82.4 days(open lung biopsy: 72.8 days, thoracoscopic lung biopsy: 99.4 days). The bronchoscopy was performed in 272 cases(53.2%), bronchoalveolar lavage was performed in 123 cases(24.1%) and percutaneous lung biopsy was performed in 72 cases(14.1%) before open or thoracoscopic lung biopsy. 4) There were 230 cases(45.0%) of interstitial lung disease, 133 cases(26.0%) of thoracic malignancies, 118 cases(23.1%) of infectious lung disease including tuberculosis and 30 cases (5.9 %) of other lung diseases including congenital anomalies. No significant differences were noted in diagnostic rate and disease characteristics between open lung biopsy and thoracoscopic lung biopsy. 5) The final diagnosis through an open or thoracoscopic lung biopsy was as same as the presumptive diagnosis before the biopsy in 302 cases(59.2%). The identical diagnostic rate was 66.5% in interstitial lung diseases, 58.7% in thoracic malignancies, 32.7% in lung infections, 55.1 % in pulmonary tuberculosis, 62.5% in other lung diseases including congenital anomalies. 6) One days after lung biopsy, $PaCO_2$ was increased from the prebiopsy level of $38.9{\pm}5.8mmHg$ to the $40.2{\pm}7.1mmHg$(P<0.05) and $PaO_2/FiO_2$ was decreased from the prebiopsy level of $380.3{\pm}109.3mmHg$ to the $339.2{\pm}138.2mmHg$(P=0.01). 7) There was a 10.1 % of complication after lung biopsy. The complication rate in open lung biopsy was much higher than in thoracoscopic lung biopsy(12.4% vs 5.8%, P<0.05). The incidence of complication was pneumothorax(23 cases, 4.6%), hemothorax(7 cases, 1.4%), death(6 cases, 1.2%) and others(15 cases, 2.9%). 8) The 5 cases of death due to lung biopsy were associated with open lung biopsy and one fatal case did not describe the method of lung biopsy. The underlying disease was 3 cases of thoracic malignancies(2 cases of bronchoalveolar cell cancer and one malignant mesothelioma), 2 cases of metastatic lung cancer, and one interstitial lung disease. The duration between open lung biopsy and death was $15.5{\pm}9.9$ days. 9) Despite the lung biopsy, 19 cases (3.7%) could not diagnosed. These findings were caused by biopsy was taken other than target lesion(5 cases), too small size to interpretate(3 cases), pathologic inability(11 cases). 10) The contribution of open or thoracoscopic lung biopsy to the final diagnosis was defininitely helpful(334 cases, 66.5%), moderately helpful(140 cases, 27.9%), not helpful or impossible to judge(28 cases, 5.6%). Overall, open or thoracoscopic lung biopsy were helpful to diagnose the lung lesion in 94.4 % of total cases. Conclusions: The open or thoracoscopic lung biopsy were relatively safe and reliable diagnostic method of lung lesion which could not diagnosed by other diagnostic approaches such as bronchoscopy. We recommend the thoracoscopic lung biopsy when the patients were in critical condition because the thoracoscopic biopsy was more safe and have equal diagnostic results compared with the open lung biopsy.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.