• Tuberculosis and Respiratory Diseases
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• v.56 no.1
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• pp.40-50
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• 2004

Background : Radiation pneumonitis(RP) is the major serious complication of thoracic irradiation treatment. In this study, we attempted to retrospectively evaluate the long-term prognosis of patients who experienced acute RP and to identify factor that might allow prediction of RP. Methods : Of the 114 lung cancer patients who underwent thoracic radiotherapy between December 2000 and December 2002, We performed analysis using a database of 90 patients who were capable of being evaluated. Results : Of the 44 patients(48.9%) who experienced clinical RP in this study, the RP was mild in 33(36.6%) and severe in 11(12.3%). All of severe RP were treated with corticosteroids. The median starting corticosteroids dose was 34 mg(30~40) and median treatment duration was 68 days(8~97). The median survival time of the 11 patients who experienced severe RP was significantly poorer than the mild RP group. (p=0.046) The higher total radiation dose(${\geq}60Gy$) was significantly associated with developing in RP.(p=0.001) The incidence of RP did not correlate with any of the ECOG performance, pulmonary function test, age, cell type, history of smoking, radiotherapy combined with chemotherapy, once-daily radiotherapy dose fraction. Also, serum albumin level, uric acid level at onset of RP did not influence the risk of severe RP in our study. Conclusion : Only the higher total radiation dose(${\geq}60Gy$) was a significant risk factor predictive of RP. Also severe RP was an adverse prognostic factor.

• Journal of Internet Computing and Services
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• v.25 no.4
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• pp.121-130
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• 2024

As internet and communication technology (ICT) is improved exponentially, types and amount of available data also increase. Even though data analysis including statistics is significant to utilize this large amount of data, there are inevitable limits to process various and complex data in general way. Meanwhile, there are many attempts to apply machine learning (ML) in various fields to solve the problems according to the enhancement in computational performance and increase in demands for autonomous systems. Especially, data processing for the model input and designing the model to solve the objective function are critical to achieve the model performance. Data processing methods according to the type and property have been presented through many studies and the performance of ML highly varies depending on the methods. Nevertheless, there are difficulties in deciding which data processing method for data analysis since the types and characteristics of data have become more diverse. Specifically, multi-variate data processing is essential for solving non-linear problem based on ML. In this paper, we present a multi-variate tabular data processing scheme for ML-aided data analysis by using Titanic dataset from Kaggle including various kinds of data. We present the methods like input variable filtering applying statistical analysis and normalization according to the data property. In addition, we analyze the data structure using visualization. Lastly, we design an ML model and train the model by applying the proposed multi-variate data process. After that, we analyze the passenger's survival prediction performance of the trained model. We expect that the proposed multi-variate data processing and visualization can be extended to various environments for ML based analysis.