• Radiation Oncology Journal
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• v.18 no.4
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• pp.300-308
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• 2000

Purpose :The aim of this study is to analysis of suwival and recurrence rates of the uterine cervical carcinoma patients whom received the radiation therapy respectively. The prognostic factors, such as Papanicolaou (Pap) smear, carcinoembriogenic antigen (CEA) and squamous cell carcinoma (SCC) antigen has been studied. Methods and Materials : From January 1981 to December 1998, eight-hundred twenty-seven uterine carvical cancer patients were treat with radiation therapy. All of the patients were divided into two groups : the radiation therapy only (S2l patients) group and the postoperative radiation therapy (326 patients) group. The age, treatment modality, clinical stage, histopathology, recurrence, follow-up Pap smears, CEA and SCC antigen were used as parameters for the evaluation. The prognostic factors such as survival and recurrence rates were peformed with the Kaplan-Meier method and the Cox hazard model, respectively. Median rollow-up was 38.6 months. Results :On the radiation therapy only group, 314 patients (60$\%$) achieved complete response (CR), 47 patients (9$\%$) showed local recurrence (LR), 78 patients (15$\%$) developed distant metastasis (DM). On the Postoperative radiation therapy group, showed 276 Patients (85$\%$) CR, 8 Patients (2$\%$) LR, 37 Patients (11$\%$) DM. The 5-year survival and recurrence rates was evaluated for all parameters. The statistically significant factors for the survival rate in univariate analysis were clinical stage (p=0.0001), treatment modality (p=0.0010), recurrence (p=0.0001), Pap smear (p=0.0329), CEA (p=0.0001) and SCC antigen (p=0.0001). Conclusion: This study indicated that after treatment, the follow-up studies of Pap smear, CEA and SCC antigen were significant parameter and prediction factors for the survival and recurrence of the uterine cervical carcinoma.

• 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 Korean Society of Transportation
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• v.24 no.5 s.91
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• pp.89-108
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• 2006

Korea has been entering the ageing society as the population of age over 65 shared over 7% since the year 2000. The ageing society needs to have transportation facility considering elderly people's travel behavior. This study aims to understand the elderly people's travel behavior using recent data in Korea. The activity schedule approach begins with travel outcomes are part of an activitv scheduling decision. For tho?e approach. used discrete choice models (especially. Nested Logit Model) to address the basic modeling problem capturing decision interaction among the many choice dimensions of the immense activity schedule choice set The day activity schedule is viewed as a sot of tours and at-home activity episodes tied togather with overarching day activity pattern using the Seoul Metropolitan Area Transportation Survey data, which was conducted in June, 2002. Decisions about a specific tour in the schedule are conditioned by the choice of day activity pattern. The day activity scheduling model estimated in this study consists of tours interrelated in a day activity pattern. The day activity pattern model represents the basic decision of activity participation and priorities and places each activity in a configuration of tours and at-home episodes. Each pattern alternative is defined by the primary activity of the day, whether the primary activity occurs at home or away, and the type of tour for the primary activity. In travel mode choice of the elderly and non-workers, especially, travel cost was found to be important in understanding interpersonal variations in mode choice behavior though, travel time was found to be less important factor in choosing travel mode. In addition, although, generally, the elderly was likely to choose transit mode, private mode was preferred for the elderly over 75 years old owing to weakened physical health for such things as going up and down of stairs. Therefore. as entering the ageing society, transit mode should be invested heavily in transportation facility Planning tor improving elderly transportation service. Although the model has not yet been validated in before-and-after prediction studies. this study gives strong evidence of its behavioral soundness, current practicality. and potential for improving reliability of transportation Projects superior to those of the best existing systems in Korea.

• Journal of The Korean Society of Grassland and Forage Science
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• v.39 no.1
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• pp.1-8
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• 2019

This study was conducted to predict the energy requirements for maintenance and growth of female Korean black goats during their growth and pregnancy phases. Fifty female goats ($18.7{\pm}0.27kg$) in their growth phase with an average age of 5 months were stratified by weight and randomly assigned into 5 groups. They were fed 5 diets varying in metabolic energy (ME) [2.32 (G1), 2.49 (G2), 2.74 (G3), 2.99 (G4), and 3.24 (G5) Mcal/kg] until they were 9-month-old. After natural breeding, 50 female goats ($30.7{\pm}0.59kg$) were stratified by weight and randomly assigned into 5 groups. They were fed 5 diets varying in ME [2.32 (P1), 2.43 (P2), 2.55 (P3), 2.66 (P4), and 2.78 (P5) Mcal/kg]. The average feed intake ranged between 1.5 and 2.0% of the body weight (BW), and there was no significant difference between the treatment groups with goats in growth or pregnancy phases. Average daily gain (ADG) in diet demand during the growth phase increased with an increasing ME density and ranged from 46 to 69 g/d (p<0.01). Feed conversion ratio (FCR) improved with the ME density during the growth phase (p<0.01). The intercept of the regression equation between ME intake and ADG indicated that energy requirement for maintenance of goats during growth and pregnancy phases was $103.53kcal/BW^{0.75}$ and $102.7kcal/BW^{0.75}$, respectively. These results may serve as a basis for the establishment of goat feeding standards in Korea. Further studies are required to assess the nutrient requirement of goats using various methods for improving accuracy.

• Journal of Korean Society of Forest Science
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• v.109 no.4
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• pp.477-483
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• 2020

We constructed a stand yield table for Chamaecyparis obtusa based on data from an actual forest. The previous stand yield table had a number of disadvantages because it was based on actual forest information. In the present study we used data from more than 200 sampling plots in a stand of Chamaecyparis obtusa. The analysis included theestimation, recovery and prediction of the distribution of values for diameter at breast height (DBH), and the result is a valuable process for the preparation ofstand yield tables. The DBH distribution model uses a Weibull function, and the site index (base age: 30 years), the standard for assessing forest productivity, was derived using the Chapman-Richards formula. Several estimation formulas for the preparation of the stand yield table were considered for the fitness index, and the optimal formula was chosen. The analysis shows that the site index is in the range of 10 to 18 in the Chamaecyparis obtusa stand. The estimated stand volume of each sample plot was found to have an accuracy of 62%. According to the residuals analysis, the stands showed even distribution around zero, which indicates that the results are useful in the field. Comparing the table constructed in this study to the existing stand yield table, we found that our table yielded comparatively higher values for growth. This is probably because the existing analysis data used a small amount of research data that did not properly reflect. We hope that the stand yield table of Chamaecyparis obtusa, a representative species of southern regions, will be widely used for forest management. As these forests stabilize and growth progresses, we plan to construct an additional yield table applicable to the production of developed stands.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1723-1735
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• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.

• Korean Circulation Journal
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• v.53 no.4
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• pp.254-267
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• 2023

Background and Objectives: Although the shortage of donor is a common problem worldwide, a significant portion of unutilized hearts are classified as marginal donor (MD) hearts. However, research on the correlation between the MD and the prognosis of heart transplantation (HTx) is lacking. This study was conducted to investigate the clinical impact of MD in HTx. Methods: Consecutive 73 HTxs during 2014 and 2021 in a tertiary hospital were analyzed. MD was defined as follows; a donor age >55 years, left ventricular ejection fraction <50%, cold ischemic time >240 minutes, or significant cardiac structural problems. Preoperative characteristics and postoperative hemodynamic data, primary graft dysfunction (PGD), and the survival rate were analyzed. Risk stratification by Index for Mortality Prediction after Cardiac Transplantation (IMPACT) score was performed to examine the outcomes according to the recipient state. Each group was sub-divided into 2 risk groups according to the IMPACT score (low <10 vs. high ≥10). Results: A total of 32 (43.8%) patients received an organ from MDs. Extracorporeal membrane oxygenation was more frequent in the non-MD group (34.4% vs. 70.7, p=0.007) There was no significant difference in PGD, 30-day mortality and long-term survival between groups. In the subgroup analysis, early outcomes did not differ between low- and high-risk groups. However, the long-term survival was better in the low-risk group (p=0.01). Conclusions: The outcomes of MD group were not significantly different from non-MD group. Particularly, in low-risk recipient, the MD group showed excellent early and long-term outcomes. These results suggest the usability of selected MD hearts without increasing adverse events.

• Journal of the Korean Society of Radiology
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• v.84 no.1
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• pp.185-196
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• 2023

Purpose This study aimed to investigate radiomics analysis of ultrasonographic images to develop a potential biomarker for predicting lymph node metastasis in papillary thyroid carcinoma (PTC) patients. Materials and Methods This study included 431 PTC patients from August 2013 to May 2014 and classified them into the training and validation sets. A total of 730 radiomics features, including texture matrices of gray-level co-occurrence matrix and gray-level run-length matrix and single-level discrete two-dimensional wavelet transform and other functions, were obtained. The least absolute shrinkage and selection operator method was used for selecting the most predictive features in the training data set. Results Lymph node metastasis was associated with the radiomics score (p < 0.001). It was also associated with other clinical variables such as young age (p = 0.007) and large tumor size (p = 0.007). The area under the receiver operating characteristic curve was 0.687 (95% confidence interval: 0.616-0.759) for the training set and 0.650 (95% confidence interval: 0.575-0.726) for the validation set. Conclusion This study showed the potential of ultrasonography-based radiomics to predict cervical lymph node metastasis in patients with PTC; thus, ultrasonography-based radiomics can act as a biomarker for PTC.

• Tuberculosis and Respiratory Diseases
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• v.42 no.2
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• pp.206-217
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• 1995

Background: In patients with obstructive sleep apnea syndrome(OSAS), there are several factors increasing upper airway resistance and there is a predisposition to compromised respiratory function during waking and sleep related to constitutional factors including a tendency to obesity. Several recent studies have suggested a possible relationship between sleep apnea(SA) and systemic hypertension. But the possible pathophysiologic link between SA and hypertension is still unclear. In this study, we have examined the relationship among age, body mass index(BMI), pulmonary function parameters and polysomnographic data in patients with OSAS. And also we tried to know the difference among these parameters between hypertensive OSAS and normotensive OSAS patients. Methods: Patients underwent a full night of polysomnography and measured pulmonary function during waking. OSAS was diagnosed if patients had more than 5 apneas per hour(apnea index, AI). A careful history of previously known or present hypertension was obtained from each patient, and patients with systolic blood pressure $\geq$ 160mmHg and/or diastolic blood pressure $\geq$ 95mmHg were classified as hypertensives. Results: The noctural nadir of arterial oxygen saturation($SaO_2$ nadir) was negatively related to AI and respiratory disturbance index(RDI), and the degree of noctural oxygen desaturation(DOD) was positively related to AI and RDI. BMI contributed to AI, RDI, $SaO_2$ nadir and DOD values. And also BMI contributed to $FEV_1,\;FEV_1/FVC$ and DLco values. There was a correlation between airway resistance(Raw) and AI, and there was a inverse correlation between DLco and DOD. But there was no difference among these parameters between hypertensive OSAS and normotensive OSAS patients. Conclusion: The obesity contributed to the compromised respiratory function and the severity of OSAS. AI and RDI were important factors in the severity of hypoxia during sleep. The measurement of pulmonary function parameters including Raw and DLco may be helpful in the prediction and assessment of OSAS patients. But we could not find clear difference between hypertensive and normotensive OSAS patients.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.231-252
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• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.