• Korean Journal of Radiology
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• v.24 no.7
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• pp.660-667
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• 2023

• IE interfaces
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• v.21 no.4
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• pp.385-393
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• 2008

Corporate's survival or bankruptcy has been determined by interaction of macroeconomic environment, industrial dynamic environment and internal process of corporate. This study attempts to examine financial factors' differences that have influence on corporate's survival or bankruptcy before and after foreign exchange crisis in Korea. The first previous empirical study that researched the cause of corporate's survival or bankruptcy in the financial ratios was attempted by Altman in 1968. Recently various survival analysis models have been published. In this paper, Multiple Discriminant Analysis model is used. We divide analytical periods into before and after foreign exchange crisis and sample randomly survival or bankruptcy firms for each period. Independent variables are financial ratios which represent growth, profitability, activity, liquidity and productivity. In conclusion, this paper examines hypothesis as "There are differences of significant financial factors before and after foreign exchange crisis."

• Annual Conference of KIPS
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• 2021.11a
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• pp.779-782
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• 2021

Survival time analysis is one of the main methods used by the pathologist to prognosis for cancer patients. In this paper, we strive to estimate the individual survival time of Adenocarcinoma (ADC) lung cancer patients from pathological images by adopting the convolutional neural network called the SurvPatchV1 model. First, we extracted tissue patches from the whole-slide images (WSI) to deal with extremely large dimensions of WSI. Then the survival time of each patch is estimated through the SurvPatchV1 model. Finally, the individual survival time of each patient is computed. The proposed method is trained and tested on the subset of the NLST dataset for ADC lung cancer. The result demonstrates that our model can obtain all tissue information in lieu of only tumor information in a whole pathological image to estimate the individual survival time.

• Korean Journal of Radiology
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• v.20 no.4
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• pp.589-598
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• 2019

Objective: To evaluate whether data acquired from perfusion computed tomography (PCT) parameters can aid in the prediction of treatment outcome after palliative chemotherapy in patients with unresectable advanced gastric cancer (AGC). Materials and Methods: Twenty-one patients with unresectable AGCs, who underwent both PCT and palliative chemotherapy, were prospectively included. Treatment response was assessed according to Response Evaluation Criteria in Solid Tumors version 1.1 (i.e., patients who achieved complete or partial response were classified as responders). The relationship between tumor response and PCT parameters was evaluated using the Mann-Whitney test and receiver operating characteristic analysis. One-year survival was estimated using the Kaplan-Meier method. Results: After chemotherapy, six patients exhibited partial response and were allocated to the responder group while the remaining 15 patients were allocated to the non-responder group. Permeability surface (PS) value was shown to be significantly different between the responder and non-responder groups (51.0 mL/100 g/min vs. 23.4 mL/100 g/min, respectively; p = 0.002), whereas other PCT parameters did not demonstrate a significant difference. The area under the curve for prediction in responders was 0.911 (p = 0.004) for PS value, with a sensitivity of 100% (6/6) and specificity of 80% (12/15) at a cut-off value of 29.7 mL/100 g/min. One-year survival in nine patients with PS value > 29.7 mL/100 g/min was 66.7%, which was significantly higher than that in the 12 patients (33.3%) with PS value ≤ 29.7 mL/100 g/min (p = 0.019). Conclusion: Perfusion parameter data acquired from PCT demonstrated predictive value for treatment outcome after palliative chemotherapy, reflected by the significantly higher PS value in the responder group compared with the non-responder group.

• Journal of Korean Society of Forest Science
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• v.89 no.4
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• pp.480-487
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• 2000

Repeated measurement of 472 permanent subplots in slash pine (Pinus elliottii Engelm.) plantations were used to develop survival prediction equations for predicting future number of planted slash pine trees. On the average, about 40 percent of the slash pines in the experimental sites had a stem cankers due to fusiform rust (Cronartium quercuum [Berk.] Miyabe ex Shirai f. sp. fusiforme) incidence. A stand level survival prediction model was developed that incorporated the incidence of fusiform rust and allowed the transition paths of trees from an uninfected stage to an infected stage. Predicted total surviving number of trees is obtained by adding together the predicted number of infected and uninfected trees. The influence of natural hardwood density and site quality on slash pine survivals tended to show a negative effects on future survivals.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.2
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• pp.47-55
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• 2011

Hemorrhagic shock is a cause of one third of death resulting from injury in the world. Early diagnosis of hemorrhagic shock makes it possible for physician to treat successfully. The objective of this paper was to select an optimal classifier model using physiological signals from rats measured during hemorrhagic experiment. This data set was used to train and predict survival rate using artificial neural network (ANN) and support vector machine (SVM). To avoid over-fitting, we chose the best classifier according to performance measured by a 10-fold cross validation method. As a result, we selected ANN having three hidden nodes with one hidden layer and SVM with Gaussian kernel function as trained prediction model, and the ANN showed 88.9 % of sensitivity, 96.7 % of specificity, 92.0 % of accuracy and the SVM provided 97.8 % of sensitivity, 95.0 % of specificity, 96.7 % of accuracy. Therefore, SVM was better than ANN for survival prediction.

• Korean Journal of Radiology
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• v.22 no.3
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• pp.425-434
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• 2021

Objective: To investigate the potential value of ¹⁸F-fluorodeoxyglucose (FDG) PET/CT in predicting the survival of patients with primary tracheal malignant tumors. Materials and Methods: An analysis of FDG PET/CT findings in 37 primary tracheal malignant tumor patients with a median follow-up period of 43.2 months (range, 10.8-143.2 months) was performed. Cox proportional hazards regression analyses were used to assess the associations between quantitative ¹⁸F-FDG PET/CT parameters, other clinic-pathological factors, and overall survival (OS). A risk prognosis model was established according to the independent prognostic factors identified on multivariate analysis. A survival curve determined by the Kaplan-Meier method was used to assess whether the prognosis prediction model could effectively stratify patients with different risks factors. Results: The median survival time of the 37 patients with tracheal tumors was 38.0 months, with a 95% confidence interval of 10.8 to 65.2 months. The 3-year, 5-year and 10-year survival rate were 54.1%, 43.2%, and 16.2%, respectively. The metabolic tumor volume (MTV), total lesion glycolysis (TLG), maximum standardized uptake value, age, pathological type, extension categories, and lymph node stage were included in multivariate analyses. Multivariate analysis showed MTV (p = 0.011), TLG (p = 0.020), pathological type (p = 0.037), and extension categories (p = 0.038) were independent prognostic factors for OS. Additionally, assessment of the survival curve using the Kaplan-Meier method showed that our prognosis prediction model can effectively stratify patients with different risks factors (p < 0.001). Conclusion: This study shows that ¹⁸F-FDG PET/CT can predict the survival of patients with primary tracheal malignant tumors. Patients with an MTV > 5.19, a TLG > 16.94 on PET/CT scans, squamous cell carcinoma, and non-E1 were more likely to have a reduced OS.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.2
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• pp.1-9
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• 2023

Research using AI and big data is also being actively conducted in the health and medical fields such as disease diagnosis and treatment. Most of the existing research data used cohort data from research institutes or some patient data. In this paper, the difference in the prediction rate of survival and the factors affecting survival between breast cancer patients in their 40~50s and other age groups was revealed using health insurance review claim data held by the HIRA. As a result, the accuracy of predicting patients' survival was 0.93 on average in their 40~50s, higher than 0.86 in their 60~80s. In terms of that factor, the number of treatments was high for those in their 40~50s, and age was high for those in their 60~80s. Performance comparison with previous studies, the average precision was 0.90, which was higher than 0.81 of the existing paper. As a result of performance comparison by applied algorithm, the overall average precision of Decision Tree, Random Forest, and Gradient Boosting was 0.90, and the recall was 1.0, and the precision of multi-layer perceptrons was 0.89, and the recall was 1.0. I hope that more research will be conducted using machine learning automation(Auto ML) tools for non-professionals to enhance the use of the value for health insurance review claim data held by the HIRA.

• Nuclear Engineering and Technology
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• v.54 no.8
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• pp.3027-3033
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• 2022

Background: In this study, various types of deep-learning models for predicting in vitro radiosensitivity from gene-expression profiling were compared. Methods: The clonogenic surviving fractions at 2 Gy from previous publications and microarray gene-expression data from the National Cancer Institute-60 cell lines were used to measure the radiosensitivity. Seven different prediction models including three distinct multi-layered perceptrons (MLP), four different convolutional neural networks (CNN) were compared. Folded cross-validation was applied to train and evaluate model performance. The criteria for correct prediction were absolute error < 0.02 or relative error < 10%. The models were compared in terms of prediction accuracy, training time per epoch, training fluctuations, and required calculation resources. Results: The strength of MLP-based models was their fast initial convergence and short training time per epoch. They represented significantly different prediction accuracy depending on the model configuration. The CNN-based models showed relatively high prediction accuracy, low training fluctuations, and a relatively small increase in the memory requirement as the model deepens. Conclusion: Our findings suggest that a CNN-based model with moderate depth would be appropriate when the prediction accuracy is important, and a shallow MLP-based model can be recommended when either the training resources or time are limited.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.17
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• pp.7463-7466
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• 2015

Aims: To analyse the predictors of recurrence, disease free survival and overall survival in cases with endometrial cancer. Materials and Methods: A total of 152 women diagnosed with endometrial cancer were screened using a prospectively collected database including age, smoking history, menopausal status, body mass index, CA125, systemic disorders, tumor histology, tumor grade, lymphovascular space invasion, tumor diameter, cervical involvement, myometrial invasion, adnexal metastases, positive cytology, serosal involvement, other pelvic metastases, type of surgery, fertility sparing approach to assess their ability to predict recurrence, disease free survival and overall survival. Results: In ROC analyses tumor diameter was a significant predictor of recurrence (AUC:0.771, P<0.001). The optimal cut off value was 3.75 with 82% sensitivity and 63% specificity. In correlation analyses tumor grade (r=0.267, p=0.001), tumor diameter (r=0.297, p<0.001) and the serosal involvement (r=0.464, p<0.001) were found to significantly correlate with the recurrence. In Cox regression analyses when some different combinations of variables included in the model which are found to be significantly associated with the presence of recurrence, tumor diameter was found to be a significant confounder for disease free survival (OR=1.2(95 CI,1.016-1.394, P=0.031). On Cox regression for overall survival only serosal involvement was found to be a significant predictor (OR=20.8 (95 % CI 2.4-179.2, P=0.006). In univariate analysis of tumor diameter > 3.75 cm and the recurrence, there was 14 (21.9 %) cases with recurrence in group with high tumor diameter where as only 3 (3.4 %) cases group with smaller tumor size (Odds ratio:7.9 (95 %CI 2.2-28.9, p<0.001). Conclusions: Although most of the significantly correlated variables are part of the FIGO staging, tumor diameter was also found to be predictor for recurrence with higher values than generally accepted.