• Asian Pacific Journal of Cancer Prevention
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• v.13 no.1
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• pp.97-101
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• 2012

Objective: With the background of aging population in China and advances in clinical medicine, the amount of operations on old patients increases correspondingly, which imposes increasing challenges to critical care medicine and geriatrics. The study was designed to describe information on the length of ICU stay from a single institution experience of old critically ill gastric cancer patients after surgery and the framework of incorporating data-mining techniques into the prediction. Methods: A retrospective design was adopted to collect the consecutive data about patients aged 60 or over with a gastric cancer diagnosis after surgery in an adult intensive care unit in a medical university hospital in Shenyang, China, from January 2010 to March 2011. Characteristics of patients and the length their ICU stay were gathered for analysis by univariate and multivariate Cox regression to examine the relationship with potential candidate factors. A regression tree was constructed to predict the length of ICU stay and explore the important indicators. Results: Multivariate Cox analysis found that shock and nutrition support need were statistically significant risk factors for prolonged length of ICU stay. Altogether, eight variables entered the regression model, including age, APACHE II score, SOFA score, shock, respiratory system dysfunction, circulation system dysfunction, diabetes and nutrition support need. The regression tree indicated comorbidity of two or more kinds of shock as the most important factor for prolonged length of ICU stay in the studied sample. Conclusions: Comorbidity of two or more kinds of shock is the most important factor of length of ICU stay in the studied sample. Since there are differences of ICU patient characteristics between wards and hospitals, consideration of the data-mining technique should be given by the intensivists as a length of ICU stay prediction tool.

• Journal of the Korean Data and Information Science Society
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• v.23 no.2
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• pp.257-269
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• 2012

Global crisis expedites the change in the environment of industry and puts small size enterprises in danger of mass bankruptcy. Because of this, domestic small size enterprises is an urgent need of restructuring. Based on the small business data registered in the Credit Guarantee Fund, we estimated the survival probability in the context of the survival analysis. We also analyzed the survival time which are distinguished depending on the types of business in the small business. Financial variables were also conducted using COX regression analysis of small businesses by types of business. In terms of types of business wholesale and retail trade industry and services were relatively high in the survival probability than light, heavy, and the construction industries. Especially the construction industry showed the lowest survival probability. In addition, we found that construction industry, the bigger BIS (bank of international settlements capital ratio) and current ratio are, the smaller default-rate is. But the bigger borrowing bond is, the bigger default-rate is. In the light industry, the bigger BIS and ROA (return on assets) are, the smaller a default-rate is. In the wholesale and retail trade industry, the bigger bis and current ratio are, the smaller a default-rate is. In the heavy industry, the bigger BIS, ROA, current ratio are, the smaller default-rate is. Finally, in the services industry, the bigger current ratio is, the smaller a default-rate is.

• Genomics & Informatics
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• v.16 no.4
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• pp.32.1-32.7
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• 2018

Ovarian cancer is one of the leading causes of cancer-related deaths in gynecological malignancies. Over 70% of ovarian cancer cases are high-grade serous ovarian cancers and have high death rates due to their resistance to chemotherapy. Despite advances in surgical and pharmaceutical therapies, overall survival rates are not good, and making an accurate prediction of the prognosis is not easy because of the highly heterogeneous nature of ovarian cancer. To improve the patient's prognosis through proper treatment, we present a prognostic prediction model by integrating high-dimensional RNA sequencing data with their clinical data through the following steps: gene filtration, pre-screening, gene marker selection, integrated study of selected gene markers and prediction model building. These steps of the prognostic prediction model can be applied to other types of cancer besides ovarian cancer.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.11
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• pp.5505-5510
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• 2012

Objective: The aim of this retrospective study is to analyze the clinical and pathological factors related to the prognosis of Chinese patients with stage Ib to IIb cervical cancer. Methods and Results: 13 clinical pathological factors in 255 patients with stage Ib to IIb cervical cancer undergoing radical hysterectomy and systematic lymphadenectomy were analyzed to screen for factors related to prognosis. The cumulative 5-year survival of the 255 patients was 75.7%. The result of the univariate analysis suggested that clinical stage, cell differentiation, depth of cervical stromal invasion, parametrial tissue involvement, and lymph node metastasis were prognostic factors for patients with stage Ib to IIb cervical cancer (P<0.05). Compared with cases with involvement of iliac nodes, obturator nodes, or inguinal lymph nodes, cases with metastasis to the common iliac lymph nodes had a poorer prognosis (P<0.05). Cases with involvement of four or more lymph nodes had a poorer prognosis than those with involvement of three or fewer lymph nodes (P<0.05). Using multivariate Cox proportional hazards model regression analysis, non-squamous histological type, poor differentiation, parametrial tissue involvement, and outer 1/3 stromal invasion were found to be independently related to patients poor prognosis (P<0.05). Conclusion: Non-squamous histological type, poor cell differentiation, parametrial tissue involvement, and outer 1/3 stromal invasion are the independent poor prognostic factors for patients with stage Ib to IIb cervical cancer.

• Journal of Labour Economics
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• v.26 no.3
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• pp.181-207
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• 2003

The purpose of this study was to conduct a dynamic analysis of married women's return to the workforce following first childbirth. We have based our investigation on the data compiled by the KLIPS, where the workforce performance is the focal point of it's research, and by these materials, this study has analyzed the aspects of the factors that decide post-childbirth return to the workforce. We have applied the Cox Regression Hazard Model, where corroborative evidence are statistically applied. The following are the conclusions that were derived from this research: First, according to the study, academic background is a vital factor in reducing the gap and time of women's return to the workforce. Second, whether having active child-care after giving birth or not doubles the chances of women returning to the workforce. Third, if the pre-birth employment form was a wage-work and the rate of returning to the workforce was lower than the non-wage, relatively speaking, this reflects that the non-wage form of work, which provides a better possibility to return to the workforce after giving birth, could be another way to prevent women's career discontinuation.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.15
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• pp.6029-6033
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• 2014

The burden and severity of a cancer can be reflected by patterns of survival. Breast cancer prognosis between two countries with a different socioeconomic status and cultural beliefs may exhibit wide variation. This study aimed to describe survival in patients with breast cancer in China and Thailand in relation to demographic and clinical prognostic information. Materials and Methods: We compared the survival of 1,504 Chinese women in Yunnan province and 929 Thai women in Songkhla with breast cancer from 2006 to 2010. Descriptive prognostic comparisons between the Chinese and Thai women were performed by relative survival analysis. A Cox regression model was used to calculate the hazard ratios of death, taking into account the age, disease stage, period of diagnosis and country. Results: The overall 5-year survival proportion for patients diagnosed with breast cancer for Yunnan province (0.72) appeared slightly better than Songkhla (0.70) without statistical significance. Thai women diagnosed with distant and regional breast cancer had poorer survival than Chinese women. Disease stage was the most important determinant of survival from the results of Cox regression model. Conclusions: Breast cancer patients in Kunming had slightly greater five-year survival rate than patients in Songkhla. Both Chinese and Thai women need improvement in prognosis, which could conceivably be attained through increased public education and awareness regarding early detection and compliance to treatment protocols.

• Journal of Preventive Medicine and Public Health
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• v.45 no.1
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• pp.14-20
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• 2012

Objectives: The aim of this study was to evaluate and quantify the risk of both individual and combined health behaviors on premature mortality in middle aged men in Korea. Methods: In total, 14 533 male subjects 40 to 59 years of age were recruited. At enrollment, subjects completed a baseline questionnaire, which included information about socio-demographic factors, past medical history, and life style. During the follow-up period from 1993 to 2008, we identified 990 all-cause premature deaths using national death certificates. A Cox proportional hazard regression model was used to estimate the hazard ratio (HR) of each health risk behavior, which included smoking, drinking, physical inactivity, and lack of sleep hours. Using the Cox model, each health behavior was assigned a risk score proportional to its regression coefficient value. Health risk scores were calculated for each patient and the HR of all-cause premature mortality was calculated according to risk score. Results: Current smoking and drinking, high body mass index, less sleep hours, and less education were significantly associated with all-cause premature mortality, while regular exercise was associated with a reduced risk. When combined by health risk score, there was a strong trend for increased mortality risk with increased score (p-trend < 0.01). When compared with the 1-9 score group, HRs of the 10-19 and 20-28 score groups were 2.58 (95% confidence intervals [CIs], 2.19 to 3.03) and 7.09 (95% CIs, 5.21 to 9.66), respectively. Conclusions: Modifiable risk factors, such as smoking, drinking, and regular exercise, have considerable impact on premature mortality and should be assessed in combination.

• The Journal of Advanced Prosthodontics
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• v.14 no.4
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• pp.223-235
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• 2022

PURPOSE. To compare the clinical outcomes of two types of implant restoration for posterior edentulous area, 3-unit bridge supported by 2 implants and 3 implant-supported splinted crowns. MATERIALS AND METHODS. The data included 127 implant-supported fixed restorations in 85 patients: 37 restorations of 3-unit bridge supported by 2 implants (2-IB), 37 restorations of 3 implant-supported splinted crowns (3-IC), and 53 single restorations (S) as controls. Peri-implantitis and mechanical complications that occurred for 14 years were analyzed by multivariable Cox regression model. Kaplan-Meier curves and the multivariable Cox regression model were used to analyze the success and survival of implants. RESULTS. Peri-implantitis occurred in 28.4% of 2-IB group, 37.8% of 3-IC group, and 28.3% of S control group with no significant difference. According to the implant position, middle implants (P2) of the 3-IC group had the highest risk of peri-implantitis. The 3-IC group showed a lower mechanical complication rate (7.2%) than the 2-IB (16.2%) and S control group (20.8%). The cumulative success rate was 52.8% in S (control) group, 62.2% in 2-IB group, and 60.4% in 3-IC group. The cumulative survival rate was 98.1% in S (control) group, 98.6% in 2-IB group, and 95.5% in 3-IC group. There was no significant difference in the success and survival rate according to the restoration type. CONCLUSION. The restoration type was not associated with the success and survival of implants. The risk of mechanical complications was reduced in 3 implant-supported splinted crowns. However, the middle implants of the 3 implant-supported splinted crowns had a higher risk of peri-implantitis.

• Journal of Periodontal and Implant Science
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• v.53 no.3
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• pp.184-193
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• 2023

Purpose: The present study was designed to compare the incidence of periodontitis according to menopausal status and to investigate the possible effect of hormone replacement therapy (HRT) on periodontitis in postmenopausal women using a common data model (CDM) at a single institution. Methods: This study involved retrospective cohort data of 950,751 patients from a 20-year database (2001 to 2020) of Ewha Womans University Mokdong Hospital converted to the Observational Medical Outcomes Partnership CDM. One-way analysis of variance models and the χ² test were used to analyze the statistical differences in patient characteristics among groups. A time-dependent Cox regression analysis was used to calculate hazard ratios and 95% confidence intervals, and P values less than 0.05 were considered to indicate statistical significance. Results: Of the 29,729 patients, 1,307 patients were diagnosed with periodontitis and 28,422 patients were not. Periodontitis was significantly more common among postmenopausal patients regardless of HRT status than among the non-menopausal group (P<0.05). Time-dependent Cox regression analysis showed that the postmenopausal patients had a significantly higher chance of having periodontitis than non-menopausal patients (P<0.05), but after adjustment for age, body mass index, and smoking status, the difference between the non-menopausal and post-menopausal HRT-treated groups was insignificant (P=0.140). Conclusions: Postmenopausal women had a significantly greater risk of periodontitis than non-menopausal women. Additionally, the use of HRT in postmenopausal women could reduce the incidence of periodontitis.

• Industry Promotion Research
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• v.7 no.1
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• pp.1-8
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• 2022

We investigated various types of machine learning methods that can be applied to censored data. Exploratory data analysis reveals the distribution of each feature, relationships among features. Next, classification problem has been set up where the dependent variable is death_event while the rest of the features are independent variables. After applying various machine learning methods to the data, it has been found that just like many other reports from the artificial intelligence arena random forest performs better than logistic regression. But recently well performed artificial neural network and gradient boost do not perform as expected due to the lack of data. Finally Kaplan-Meier and Cox proportional hazard model have been employed to explore the relationship of the dependent variable (t_i, δ_i) with the independent variables. Also random forest which is used in machine learning has been applied to the survival analysis with censored data.