• Tuberculosis and Respiratory Diseases
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• v.87 no.3
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• pp.349-356
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• 2024

Background: Acute pulmonary embolism (APE) is a fatal disease with varying clinical characteristics and imaging. The aim of this study was to define the clinical characteristics, risk factors, and outcomes in patients with APE at a university hospital in Thailand. Methods: Patients diagnosed with APE and admitted to our institute between January 1, 2017 and December 31, 2022 were retrospectively enrolled. The clinical characteristics, investigations, and outcomes were recorded. Results: Over the 6-year study period, 369 patients were diagnosed with APE. The mean age was 65 years; 64.2% were female. The most common risk factor for APE was malignancy (46.1%). In-hospital mortality rate was 23.6%. The computed tomography pulmonary artery revealed the most proximal clots largely in segmental pulmonary artery (39.0%), followed by main pulmonary artery (36.3%). This distribution was consistent between survivors and non-survivors. Multivariate logistic regression analysis revealed that APE mortality was associated with active malignancy, higher serum creatinine, lower body mass index (BMI), and tachycardia with adjusted odds ratio (95% confidence interval [CI]) of 3.70 (1.59 to 8.58), 3.54 (1.35 to 9.25), 2.91 (1.26 to 6.75), and 2.54 (1.14 to 5.64), respectively. The prediction model was constructed with area under the curve of 0.77 (95% CI, 0.70 to 0.84). Conclusion: The overall mortality rate among APE patients was 23.6%, with APE-related death accounting for 5.1%. APE mortality was associated with active malignancy, higher serum creatinine, lower BMI, and tachycardia.

• Tuberculosis and Respiratory Diseases
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• v.67 no.2
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• pp.105-112
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• 2009

Background: Solitary pulmonary nodules (SPN) are encountered incidentally in 0.2% of patients who undergo chest X-ray or chest CT. Although SPN has malignant potential, it cannot be treated surgically by biopsy in all patients. The first stage is to determine if patients with SPN require periodic observation and biopsy or resection. An important early step in the management of patients with SPN is to estimate the clinical pretest probability of a malignancy. In every patient with SPN, it is recommended that clinicians estimate the pretest probability of a malignancy either qualitatively using clinical judgment or quantitatively using a validated model. This study examined whether Bayesian analysis or multiple logistic regression analysis is more predictive of the probability of a malignancy in SPN. Methods: From January 2005 to December 2008, this study enrolled 63 participants with SPN at the Kangnam Sacred Hospital. The accuracy of Bayesian analysis and Bayesian analysis with a FDG-PET scan, and Multiple logistic regression analysis was compared retrospectively. The accurate probability of a malignancy in a patient was compared by taking the chest CT and pathology of SPN patients with <30 mm at CXR incidentally. Results: From those participated in study, 27 people (42.9%) were classified as having a malignancy, and 36 people were benign. The result of the malignant estimation by Bayesian analysis was 0.779 (95% confidence interval [CI], 0.657 to 0.874). Using Multiple logistic regression analysis, the result was 0.684 (95% CI, 0.555 to 0.796). This suggests that Bayesian analysis provides a more accurate examination than multiple logistic regression analysis. Conclusion: Bayesian analysis is better than multiple logistic regression analysis in predicting the probability of a malignancy in solitary pulmonary nodules but the difference was not statistically significant.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.83-102
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• 2021

The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.

• Korean small business review
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• v.31 no.2
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• pp.103-120
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• 2009

As business incubation centers (BICs) have been operating for more than 10 years in Korea, many early stage startups tend to use the services provided by the incubating centers. BICs in Korea have accumulated the knowledge and experience in the past ten years and their services have been considerably improved. The business incubating service has three facets : (1) business infrastructure service, (2) direct service, and (3) indirect service. The mission of BICs is to provide the early stage entrepreneurs with the incubating service in a limited period time to help them grow strong enough to survive the fierce competition after graduating from the incubation. However, the incubating services sometimes fail to foster the independence of new startup companies, and raise the dependence of many companies on BICs. Thus, the dependence on BICs is a very important factor to understand the survival of the incubated startup companies after graduation from BICs. The purpose of this study is to identify the main factors that influence the firm's dependence on BICs and to characterize the relationships among the identified factors. The business incubating service is a core construct of this study. It includes various activities and resources, such as offering the physical facilities, legal service, and connecting them with outside organizations. These services are extensive and take various forms. They are provided by BICs directly or indirectly. Past studies have identified various incubating services and classify them in different ways. Based on the past studies, we classify the business incubating service into three categories as mentioned above : (1) business infrastructure support, (2) direct support, and (3) networking support. The business infrastructure support is to provide the essential resources to start the business, such as physical facilities. The direct support is to offer the business resources available in the BICs, such as human, technical, and administrational resources. Finally, the indirect service was to support the resource in the outside of business incubation center. Dependence is generally defined as the degree to which a client firm needs the resources provided by the service provider in order to achieve its goals. Dependence is generated when a firm recognizes the benefits of interacting with its counterpart. Hence, the more positive outcomes a firm derives from its relationship with the partner, the more dependent on the partner the firm must inevitably become. In business incubating, as a resident firm is incubated in longer period, we can predict that her dependence on BICs would be stronger. In order to foster the independence of the incubated firms, BICs have to be able to manipulate the provision of their services to control the firms' dependence on BICs. Based on the above discussion, the research model for relationships between dependence and its affecting factors was developed. We surveyed the companies residing in BICs to test our research model. The instrument of our study was modified, in part, on the basis of previous relevant studies. For the purposes of testing reliability and validity, preliminary testing was conducted with firms that were residing in BICs and incubated by the BICs in the region of Gwangju and Jeonnam. The questionnaire was modified in accordance with the pre-test feedback. We mailed to all of the firms that had been incubated by the BICs with the help of business incubating managers of each BIC. The survey was conducted over a three week period. Gifts (of approximately ₩10,000 value) were offered to all actively participating respondents. The incubating period was reported by the business incubating managers, and it was transformed using natural logarithms. A total of 180 firms participated in the survey. However, we excluded 4 cases due to a lack of consistency using reversed items in the answers of the companies, and 176 cases were used for the analysis. We acknowledge that 176 samples may not be sufficient to conduct regression analyses with 5 research variables in our study. Each variable was measured through multiple items. We conducted an exploratory factor analysis to assess their unidimensionality. In an effort to test the construct validity of the instruments, a principal component factor analysis was conducted with Varimax rotation. The items correspond well to each singular factor, demonstrating a high degree of convergent validity. As the factor loadings for a variable (or factor) are higher than the factor loadings for the other variables, the instrument's discriminant validity is shown to be clear. Each factor was extracted as expected, which explained 70.97, 66.321, and 52.97 percent, respectively, of the total variance each with eigen values greater than 1.000. The internal consistency reliability of the variables was evaluated by computing Cronbach's alphas. The Cronbach's alpha values of the variables, which ranged from 0.717 to 0.950, were all securely over 0.700, which is satisfactory. The reliability and validity of the research variables are all, therefore, considered acceptable. The effects of dependence were assessed using a regression analysis. The Pearson correlations were calculated for the variables, measured by interval or ratio scales. Potential multicollinearity among the antecedents was evaluated prior to the multiple regression analysis, as some of the variables were significantly correlated with others (e.g., direct service and indirect service). Although several variables show the evidence of significant correlations, their tolerance values range between 0.334 and 0.613, thereby demonstrating that multicollinearity is not a likely threat to the parameter estimates. Checking some basic assumptions for the regression analyses, we decided to conduct multiple regression analyses and moderated regression analyses to test the given hypotheses. The results of the regression analyses indicate that the regression model is significant at p < 0.001 (F = 44.260), and that the predictors of the research model explain 42.6 percent of the total variance. Hypotheses 1, 2, and 3 address the relationships between the dependence of the incubated firms and the business incubating services. Business infrastructure service, direct service, and indirect service are all significantly related with dependence (β = 0.300, p < 0.001; β = 0.230, p < 0.001; β = 0.226, p < 0.001), thus supporting Hypotheses 1, 2, and 3. When the incubating period is the moderator and dependence is the dependent variable, the addition of the interaction terms with the antecedents to the regression equation yielded a significant increase in R2 (F change = 2.789, p < 0.05). In particular, direct service and indirect service exert different effects on dependence. Hence, the results support Hypotheses 5 and 6. This study provides several strategies and specific calls to action for BICs, based on our empirical findings. Business infrastructure service has more effect on the firm's dependence than the other two services. The introduction of an additional high charge rate for a graduated but allowed to stay in the BIC is a basic and legitimate condition for the BIC to control the firm's dependence. We detected the differential effects of direct and indirect services on the firm's dependence. The firms with long incubating period are more sensitive to indirect service positively, and more sensitive to direct service negatively, when assessing their levels of dependence. This implies that BICs must develop a strategy on the basis of a firm's incubating period. Last but not least, it would be valuable to discover other important variables that influence the firm's dependence in the future studies. Moreover, future studies to explain the independence of startup companies in BICs would also be valuable.

• Journal of dental hygiene science
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• v.14 no.4
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• pp.510-515
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• 2014

Despite the high demand for dental care professionals, high turnover of dental hygienists have been reported, especially among workers in the dental clinics. This study aimed to examine job satisfaction factors affecting dental hygienist's service year in Seoul using cross-sectional data. The questionnaire survey was conducted from August 26, 2013 to September 13, 2013. Data were collected from 296 employees of dental clinics and hospitals located in Seoul. Logistic regression analysis was applied for parameter estimates, using PASW Statistics 18.0 and R software version 3.0.3. The Cronbach's ${\alpha}$ for the total job satisfaction factors was 0.922. In descriptive statistics, the group (that less than four years of working and over four years of working) had a statistically significant difference in age, religion, experience of turnover and autonomous factor among job satisfaction factors. In multiple logistic regression model, autonomy in job satisfaction was an important factor to predict the length of service in dental hygienist (odds ratio, 2.65; 95% confidence interval, 1.06~6.60). Autonomous factor was a significant predictor of length of service for dental hygienist. This study encourages future investigations of the role of job satisfaction of service year using better analytical frameworks.

• Journal of Chest Surgery
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• v.53 no.3
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• pp.104-113
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• 2020

Background: Accurate intraoperative assessment of mediastinal lymph nodes is a critical aspect of lung cancer surgery. The efficacy and potential for upstaging implicit in these dissections must therefore be revisited in the current era of uniportal video-assisted thoracoscopic surgery (VATS). Methods: A retrospective study was conducted in which 544 patients with stage I (T1abc-T2a, N0, M0) primary lung cancer were analyzed. To assess risk factors for nodal upstaging and to limit any imbalance imposed by surgical choices, we constructed an inverse probability of treatment-weighted (IPTW) logistic regression model (in addition to non-weighted logistic models). We also evaluated risk factors for early locoregional recurrence using IPTW logistic regression analysis. Results: In the comparison of uniportal and multiportal VATS, the resected lymph node count (14.03±8.02 vs. 14.41±7.41, respectively; p=0.48) and rate of nodal upstaging (6.5% vs. 8.7%, respectively; p=0.51) appeared similar. Predictors of nodal upstaging included tumor size (odds ratio [OR], 1.74; 95% confidence interval [CI], 1.12-2.70), carcinoembryonic antigen level (OR, 1.11; 95% CI, 1.04-1.18), and histologically confirmed pleural invasion (OR, 3.97; 95% CI, 1.89-8.34). The risk factors for locoregional recurrence within 1 year were found to be number of resected N2 nodes, age, and nodal upstaging. Conclusion: Uniportal and multiportal VATS appear similar with regard to accuracy and thoroughness, showing no significant difference in the extent of nodal dissection.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.10
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• pp.4335-4338
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• 2015

Background: Oral cancer is the third most common malignancy in Saudi Arabia, the highest incidence of which is reported from Jazan province. The objective of this study was to evaluate the association of various locally used substances, especially shamma, with oral cancer in the Jazan region of Saudi Arabia. Materials and Methods: A hospital-based case-control study was designed and patient records were scanned for histologically confirmed oral cancer cases. Forty eight patients who were recently diagnosed with oral cancer were selected as cases. Two healthy controls were selected for each observed case and they were matched with age (+/- 5 years) gender and location. Use of different forms of tobacco such as cigarettes, pipe-smoking and shamma (smokeless-tobacco) was assessed. Khat, a commonly used chewing substance in the community was also included. Descriptive analysis was first performed followed by multiple logistic regression (with and without interaction) to derive odds ratios (ORs) and 95% confidence interval (CIs). Results: Mean age of the study sample (56% males and 44% females) was 65.3 years. Multinomial regression analysis revealed that shamma use increased the odds of developing oral cancer by 29 times (OR=29.3; 10.3-83.1). Cigarette (OR=6.74; 2.18-20.8) was also seen to have an effect. With the interaction model the odds ratio increased significantly for shamma users (OR=37.2; 12.3-113.2) and cigarette smokers (OR=10.5; 2.88-3.11). Khat was observed to have negative effect on the disease occurrence when used along with shamma (OR=0.01; 0.00 - 0.65). Conclusions: We conclude that shamma, a moist form of smokeless tobacco is a major threat for oral cancer occurrence in the Jazan region of Saudi Arabia. This study gives a direction to conduct further longitudinal studies in the region with increased sample size representing the population in order to provide more substantial evidence.

• Journal of Preventive Medicine and Public Health
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• v.55 no.5
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• pp.428-435
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• 2022

Objectives: The Indonesian Ministry of Health launched isoniazid preventive therapy (IPT) in 2016, with general practitioners (GPs) at the frontline of this program. However, the extent to which GPs have internalized this program remains uncertain. The aim of this study was to identify the knowledge and attitudes of GPs towards the IPT program in Indonesia. Methods: This study used an online, self-administered questionnaire distributed via e-mail and social messaging services. A logistic regression model was employed to identify the explanatory variables influencing the level of knowledge and attitudes toward IPT among GPs in Indonesia. An empirical analysis was conducted separately for each response variable (knowledge and attitudes). Results: Of the 418 respondents, 128 (30.6%) had a good knowledge of IPT. Working at a public hospital was the only variable associated with good knowledge, with an adjusted odds ratio (aOR) of 1.69 (95% confidence interval [CI], 1.02 to 2.81). Furthermore, 279 respondents (66.7%) had favorable attitudes toward IPT. In the adjusted logistic regression analysis, good knowledge (aOR, 0.55; 95% CI, 0.34 to 0.89), 1-5 years of work experience (aOR, 2.09; 95% CI, 1.21 to 3.60), and having experienced IPT training (aOR, 0.48; 95% CI, 0.25 to 0.93), were significantly associated with favorable attitudes. Conclusions: In general, GPs in Indonesia had favorable attitudes toward IPT. However, their knowledge of IPT was limited. GPs are an essential element of the IPT program in the country, and therefore, adequate information dissemination to improve their understanding is critical for the long-term viability and quality of the IPT program in Indonesia.

• Journal of Chest Surgery
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• v.54 no.6
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• pp.480-486
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• 2021

Background: Although various methods are already used to calculate predicted postoperative forced expiratory volume in 1 second (FEV₁) based on preoperative FEV₁ in lung surgery, the predicted postoperative FEV₁ is not always the same as the actual postoperative FEV₁. Observed postoperative FEV₁ values are usually the same or higher than the predicted postoperative FEV₁. To overcome this issue, we investigated the relationship between the number of resected lung segments and the discordance of preoperative and postoperative FEV₁ values. Methods: From September 2014 to May 2020, the data of all patients who underwent anatomical lung resection by video-assisted thoracoscopic surgery (VATS) were gathered and analyzed retrospectively. We investigated the association between the number of resected segments and the differential FEV₁ (a measure of the discrepancy between the predicted and observed postoperative FEV₁) using the t-test and linear regression. Results: Information on 238 patients who underwent VATS anatomical lung resection at Kyung Hee University Hospital at Gangdong and by DH. Kim for benign and malignant disease was collected. After applying the exclusion criteria, 114 patients were included in the final analysis. In the multiple linear regression model, the number of resected segments showed a positive correlation with the differential FEV₁ (Pearson r=0.384, p<0.001). After adjusting for multiple covariates, the differential FEV₁ increased by 0.048 (95% confidence interval, 0.023-0.073) with an increasing number of resected lung segments (R²=0.271, p<0.001). Conclusion: In this study, after pulmonary resection, the number of resected segments showed a positive correlation with the differential FEV₁.

• Journal of Preventive Medicine and Public Health
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• v.56 no.5
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• pp.467-474
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• 2023

Objectives: Diabetes mellitus (DM) is a serious public health issue that places a heavy financial, social, and health-related burden on individuals, families, and healthcare systems. Self-reported health-related quality of life (HRQoL) is extensively used for monitoring the general population's health conditions and measuring the effectiveness of interventions. Therefore, this study investigated HRQoL and associated factors among patients with type 2 DM at a primary healthcare center in Indonesia. Methods: A cross-sectional study was conducted in Klaten District, Central Java, Indonesia, from May 2019 to July 2019. In total, 260 patients with DM registered with National Health Insurance were interviewed. HRQoL was measured with the EuroQol Group's validated Bahasa Indonesia version of the EuroQoL 5-Dimension 5-Level (EQ-5D-5L) with the Indonesian value set. Multivariate regression models were used to identify factors influencing HRQoL. Results: Data from 24 patients were excluded due to incomplete information. Most participants were men (60.6%), were aged above 50 years (91.5%), had less than a senior high school education (75.0%), and were unemployed (85.6%). The most frequent health problems were reported for the pain/discomfort dimension (64.0%) followed by anxiety (28.4%), mobility (17.8%), usual activities (10.6%), and self-care (6.8%). The average EuroQoL 5-Dimension (EQ-5D) index score was 0.86 (95% confidence interval [CI], 0.83 to 0.88). In the multivariate ordinal regression model, a higher education level (coefficient, 0.08; 95% CI, 0.02 to 0.14) was a significant predictor of the EQ-5D-5L utility score. Conclusions: Patients with diabetes had poorer EQ-5D-5L utility values than the general population. DM patients experienced pain/discomfort and anxiety. There was a substantial positive relationship between education level and HRQoL.