• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.11
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• pp.7414-7423
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• 2015

Subjective cognitive-linguistic complaints in older adults contribute to the diagnostic and prognostic investigation of MCI or dementia. However, the utility of subjective test for predicting cognitive-linguistic decline is controversial. Few domestic studies have included the correlation between subjective and objective tests systematically. The current study analyzed 26 studies published since 2000, and the effect sizes of their correlation coefficients between two tests were computed. The results of qualitative analysis indicated that the number of subjects ranged from 26 to 657. Subjective tests included the self-report 75.4% and the informant-report 24.6%. In objective tests, memory comprised the largest proportion, followed by global cognition, and language, etc. As a result of meta-analysis, self-report test had the predictive value for dementia, and informant-report test contributed to discriminate among 3 groups. In the elderly group, self-report test was correlated with reasoning, and informant-report test with memory and language. In MCIs, self-report test predicted several abilities including language, and informant-report test signaled the future decline of domains like global cognition. Two types of subjective tests in dementia also represented memory, language, and global cognition accurately. This study provides evidence-based information to support relationships between subjective and objective tests for cognitive-linguistic ability in 3 groups.

• Korean Journal of Comparative Education
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• v.24 no.5
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• pp.53-70
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• 2014

This study was performed in order to suggest the future model of Asian universities that could be used in the planning of the global competitive strategy. Futurologists forecasted the future of higher education using Harman Fan Scenario as like this. First, most current universities will be 'the satellite university' until 2015. Second, they also will replace 'the bookless university' until 2020. Third, they will be 'no calendar university' until 2025. And then they may be 'all have access university' until 2030. After 2030, futurologists prospected that almost universities based on off-line campus will be disappeared into the history. The analysis method of Harman fan scenario and applied scenarios were also used to "A study on the future scenario of Korean university". The predictive model and the alternative models were explored in a view point of students, enterprise, and government. Individuality with educational excellence are standardized for learner, profit and effectiveness are applied for enterpriser, and equality with welfare are adapted for national leader. Asian universities need to focus on bringing up the practical ability based on conscious and emotional education instead of knowledge based on memory. Also they need to enforce the specialized education that can create new jobs through convergence of interdisciplinary. Especially, Asian nations need to explore, to find the strengthen area of their universities compared with USA. And these area should be specialized. The convergency strategy between oriental medicine and informatics is a meaningful sample. Based on this point, a predicted with 3 alternative scenarios in a view point of equality were suggested for the future of Asian universities.

• Journal of Gastric Cancer
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• v.21 no.3
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• pp.268-278
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• 2021

Purpose: While several prognostic models for the stratification of death risk have been developed for patients with advanced gastric cancer receiving first-line chemotherapy, they have seldom been tested in the Chinese population. This study investigated the performance of these models and identified the optimal tools for Chinese patients. Materials and Methods: Patients diagnosed with metastatic or recurrent gastric adenocarcinoma who received first-line chemotherapy were eligible for inclusion in the validation cohort. Their clinical data and survival outcomes were retrieved and documented. Time-dependent receiver operating characteristic (ROC) and calibration curves were used to evaluate the predictive ability of the models. Kaplan-Meier curves were plotted for patients in different risk groups divided by 7 published stratification tools. Log-rank tests with pairwise comparisons were used to compare survival differences. Results: The analysis included a total of 346 patients with metastatic or recurrent disease. The median overall survival time was 11.9 months. The patients were different into different risk groups according to the prognostic stratification models, which showed variability in distinguishing mortality risk in these patients. The model proposed by Kim et al. showed relative higher predicting abilities compared to the other models, with the highest χ² (25.8) value in log-rank tests across subgroups, and areas under the curve values at 6, 12, and 24 months of 0.65 (95% confidence interval [CI]: 0.59-0.72), 0.60 (0.54-0.65), and 0.63 (0.56-0.69), respectively. Conclusions: Among existing prognostic tools, the models constructed by Kim et al., which incorporated performance status score, neutrophil-to-lymphocyte ratio, alkaline phosphatase, albumin, and tumor differentiation, were more effective in stratifying Chinese patients with gastric cancer receiving first-line chemotherapy.

• Economic and Environmental Geology
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• v.52 no.2
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• pp.199-212
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• 2019

In the machine learning techniques, the sampling strategy of the training data affects a performance of the prediction model such as generalizing ability as well as prediction accuracy. Especially, in landslide susceptibility analysis, the data sampling procedure is the essential step for setting the training data because the number of non-landslide points is much bigger than the number of landslide points. However, the previous researches did not consider the various sampling methods for the training data. That is, the previous studies selected the training data randomly. Therefore, in this study the authors proposed several different sampling methods and assessed the effect of the sampling strategies of the training data in landslide susceptibility analysis. For that, total six different scenarios were set up based on the sampling strategies of landslide points and non-landslide points. Then Random Forest technique was trained on the basis of six different scenarios and the attribute importance for each input variable was evaluated. Subsequently, the landslide susceptibility maps were produced using the input variables and their attribute importances. In the analysis results, the AUC values of the landslide susceptibility maps, obtained from six different sampling strategies, showed high prediction rates, ranges from 70 % to 80 %. It means that the Random Forest technique shows appropriate predictive performance and the attribute importance for the input variables obtained from Random Forest can be used as the weight of landslide conditioning factors in the susceptibility analysis. In addition, the analysis results obtained using specific sampling strategies for training data show higher prediction accuracy than the analysis results using the previous random sampling method.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.132-145
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• 2022

This study was based on the black box images of traffic accidents on highways, cluster analysis and prediction model comparisons were carried out. As analysis data, vehicle driving behavior and road surface conditions that can grasp road and traffic conditions just before the accident were used as explanatory variables. Considering that traffic accident data is affected by many factors, cluster analysis reflecting data heterogeneity is used. Each cluster classified by cluster analysis was divided based on the ratio of the severity level of the accident, and then an accident prediction evaluation was performed. As a result of applying the Logit model, the accident prediction model showed excellent predictive ability when classifying groups by cluster analysis and predicting them rather than analyzing the entire data. It is judged that it is more effective to predict accidents by reflecting the characteristics of accidents by group and the severity of accidents. In addition, it was found that a collision accident during stopping such as a secondary accident and a side collision accident during lane change act as important driving behavior variables.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.87-96
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• 2022

The traffic vulnerable, including elderly pedestrians, have a relatively low walking speed and slow cognitive response time due to reduced physical ability. Although a smart crossing system has been developed and operated to improve problem, it is difficult to operate a signal that reflects the appropriate walking speed for each pedestrian. In this study, a neural network model and a multiple regression model-based traversing speed estimation model were developed using image information collected in an area with a high percentage of traffic vulnerability. to support the provision of optimal walking signals according to real-time traffic weakness. actual traffic data collected from the urban traffic network of Paju-si, Gyeonggi-do were used. The performance of the model was evaluated through seven selected indicators, including correlation coefficient and mean absolute error. The multiple linear regression model had a correlation coefficient of 0.652 and 0.182; the neural network model had a correlation coefficient of 0.823 and 0.105. The neural network model showed higher predictive power.

• Journal of the korean academy of Pediatric Dentistry
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• v.50 no.3
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• pp.263-276
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• 2023

The purpose of this study was to evaluate the performance of a model using You Only Look Once (YOLO) for object detection of proximal caries in periapical radiographs of children. A total of 2016 periapical radiographs in primary dentition were selected from the M6 database as a learning material group, of which 1143 were labeled as proximal caries by an experienced dentist using an annotation tool. After converting the annotations into a training dataset, YOLO was trained on the dataset using a single convolutional neural network (CNN) model. Accuracy, recall, specificity, precision, negative predictive value (NPV), F1-score, Precision-Recall curve, and AP (area under curve) were calculated for evaluation of the object detection model's performance in the 187 test datasets. The results showed that the CNN-based object detection model performed well in detecting proximal caries, with a diagnostic accuracy of 0.95, a recall of 0.94, a specificity of 0.97, a precision of 0.82, a NPV of 0.96, and an F1-score of 0.81. The AP was 0.83. This model could be a valuable tool for dentists in detecting carious lesions in periapical radiographs.

• Journal of the Korea Convergence Society
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• v.13 no.1
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• pp.399-408
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• 2022

The purpose of this study was to compare the usefulness of the lipid ratio indicators for the diagnosis of metabolic syndrome in the elderly aged 65 years or older. From January 2018 to December 2020, 1,464 people aged 65 years or older who underwent a health checkup at a general hospital in Seoul were included. Lipid ratio indicators were measured through blood tests. The prevalence of metabolic syndrome according to the quartiles of the lipid ratio index was confirmed by logistic regression analysis. In addition, the metabolic syndrome predictive ability and cutoff value of the lipid ratio indices were estimated with the receiver operating characteristic(ROC) curve. The correlation between atherogenic index of plasma(AIP) and waist circumference was the highest in both men and women(r=0.278, p<0.001 vs r=0.252, p<0.001). As for the lipid ratio indices, the incidence of metabolic syndrome was higher in the fourth quartile than in the first quartile. The area under the ROC curve(AUC) value of AIP was higher at 0.826(95% CI=0.799-0.850) and 0.852(95% CI=0.820-0.881) for men and women, respectively, compared to other lipid ratio indicators, and the optimal cutoff values for both men and women was 0.44(p<0.001). Therefore, the AIP among the lipid ratio indicators was found to be the most useful index for diagnosing metabolic syndrome in the elderly aged 65 years or older.

• Journal of Life Science
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• v.33 no.6
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• pp.490-497
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• 2023

Pregnant women may need to take medications to treat preexisting diseases or diseases that develop during pregnancy. However, some drugs may be fetotoxic and lead to, for example, teratogenicity and growth retardation. Predicting the fetotoxicity of drugs is thus important for the health of the mother and fetus. The fetotoxicity of many drugs has not been established because various challenges hinder the ability of researchers to determine their fetotoxicity. The need exists for in silico-based fetotoxicity assessment models, as they can modernize the testing paradigm, improve predictability, and reduce the use of animals and the costs of fetotoxicity testing. In this study, we collected data on the fetotoxicity of drugs and constructed fetotoxicity prediction models based on various machine learning algorithms. We optimized the models for more precise predictions by tuning the hyperparameters. We then performed quantitative performance evaluations. The results indicated that the constructed machine learning-based models had high performance (AUROC >0.85, AUPR >0.9) in fetotoxicity prediction. We also analyzed the feature importance of our model's predictions, which could be leveraged to identify the specific features of drugs that are strongly associated with fetotoxicity. The proposed model can be used to prescreen drugs and drug candidates at a lower cost and in less time. It provides a predictive score for fetotoxicity risk, which may be beneficial in the design of studies on fetotoxicity in human pregnancy.

• Knowledge Management Research
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• v.25 no.2
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• pp.19-47
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• 2024

Organizational ability to analyze and utilize data plays an important role in knowledge management and decision-making. This study aims to investigate the potential application of large language models in corporate data analysis. Focusing on the field of human resources, the research examines the data analysis capabilities of these models. Using the widely studied IBM HR dataset, the study reproduces machine learning-based employee turnover prediction analyses from previous research through ChatGPT and compares its predictive performance. Unlike past research methods that required advanced programming skills, ChatGPT-based machine learning data analysis, conducted through the analyst's natural language requests, offers the advantages of being much easier and faster. Moreover, its prediction accuracy was found to be competitive compared to previous studies. This suggests that large language models could serve as effective and practical alternatives in the field of corporate data analysis, which has traditionally demanded advanced programming capabilities. Furthermore, this approach is expected to contribute to the popularization of data analysis and the spread of data-driven decision-making (DDDM). The prompts used during the data analysis process and the program code generated by ChatGPT are also included in the appendix for verification, providing a foundation for future data analysis research using large language models.