• Tuberculosis and Respiratory Diseases
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• v.44 no.2
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• pp.401-408
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• 1997

Background : Pulmonary sequestration is a rare congenital malformation, which is manifested by formation of nonfunctioning lung tissue lacking normal communication with the tracheobronchial tree. The preoperative diagnostic rate has been relatively low, and without consideration of pulmonary sequestration, unexpected bleeding from aberrant vessels may be a serious problem during the operation. The purpose of our study is to describe the clinical features of pulmonary sequestration based on a review of 15 cases treated by operation. Method : Fifteen patients with pulmonary sequestration who had undergone surgical treatment from 1991 through May 1996 at Yongdong Severance Hospital and Severance Hospital were reviewed retrospectively. Results : The mean age of the patients was 22.5 years (range 5~57), and male to female ratio was 9 : 6. Clinical presentations varied from recurrent respiratory infections such as fever, cough, and sputum or chest pain to no symptom. The chest simple X-rays showed multicystic shadow(10/15) and solid mass-like shadow(5/15). The chest CT scans, done in twelve cases, showed multicystic lesion with or without lung infiltration(8/12), solid mass-like lesion(4/12), The chest MRIs, done in three cases, revealed the aberrant arteries originating from descending aorta(2/3). Aortograms, done in four cases, showed the aberrant arteries originating from descending thoracic aorta(2/4), abdominal aorta(I/4), and intercostal artery(1/4). and the venous returns were via the pulmonary veins. Pulmonary sequestration was considered preoperatively in six patients of fifteen. Other preliminary diagnosis were lung tumor(3/15), lung abscess(21/15), bronchiectasis(2/15), and mediastinal tumor(2/15). In the operative findings, twelve cases were of intralobar type and three cases of extralobar type. The left lower lobe was most often affected(9/15) and one extralobar sequestration was in the pericardium. The aberrant arteries originated from descending thoracic aorta(6/15), abdominal aorta(1/15), internal thoracic arteries (2/15), intercostal artery(1/15), pericardiophrenic artery(1/15), but in four cases, the origins could not be defined. There was no mortality or complication postoperatively. Conclusion : In our study, preoperative diagnostic rate was relatively low, and clinical features were similar to previous reports. Preoperative vigorous diagnostic approach including aortography is strongly advocated not only for its diagnostic value, but also for accurate localization of the aberrant vessels, which is major concern to surgical procedure.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.