• Korean Circulation Journal
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• v.53 no.11
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• pp.758-771
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• 2023

Background and Objectives: Paroxysmal atrial fibrillation (AF) is a major potential cause of embolic stroke of undetermined source (ESUS). However, identifying AF remains challenging because it occurs sporadically. Deep learning could be used to identify hidden AF based on the sinus rhythm (SR) electrocardiogram (ECG). We combined known AF risk factors and developed a deep learning algorithm (DLA) for predicting AF to optimize diagnostic performance in ESUS patients. Methods: A DLA was developed to identify AF using SR 12-lead ECG with the database consisting of AF patients and non-AF patients. The accuracy of the DLA was validated in 221 ESUS patients who underwent insertable cardiac monitor (ICM) insertion to identify AF. Results: A total of 44,085 ECGs from 12,666 patient were used for developing the DLA. The internal validation of the DLA revealed 0.862 (95% confidence interval, 0.850-0.873) area under the curve (AUC) in the receiver operating curve analysis. In external validation data from 221 ESUS patients, the diagnostic accuracy of DLA and AUC were 0.811 and 0.827, respectively, and DLA outperformed conventional predictive models, including CHARGE-AF, C2HEST, and HATCH. The combined model, comprising atrial ectopic burden, left atrial diameter and the DLA, showed excellent performance in AF prediction with AUC of 0.906. Conclusions: The DLA accurately identified paroxysmal AF using 12-lead SR ECG in patients with ESUS and outperformed the conventional models. The DLA model along with the traditional AF risk factors could be a useful tool to identify paroxysmal AF in ESUS patients.

• Computers and Concrete
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• v.34 no.2
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• pp.247-265
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• 2024

Measuring the fracture toughness of concrete in laboratory settings is challenging due to various factors, such as complex sample preparation procedures, the requirement for precise instruments, potential sample failure, and the brittleness of the samples. Therefore, there is an urgent need to develop innovative and more effective tools to overcome these limitations. Supervised learning methods offer promising solutions. This study introduces seven machine learning algorithms for predicting concrete's effective fracture toughness (K-eff). The models were trained using 560 datasets obtained from the central straight notched Brazilian disc (CSNBD) test. The concrete samples used in the experiments contained micro silica and powdered stone, which are commonly used additives in the construction industry. The study considered six input parameters that affect concrete's K-eff, including concrete type, sample diameter, sample thickness, crack length, force, and angle of initial crack. All the algorithms demonstrated high accuracy on both the training and testing datasets, with R² values ranging from 0.9456 to 0.9999 and root mean squared error (RMSE) values ranging from 0.000004 to 0.009287. After evaluating their performance, the gated recurrent unit (GRU) algorithm showed the highest predictive accuracy. The ranking of the applied models, from highest to lowest performance in predicting the K-eff of concrete, was as follows: GRU, LSTM, RNN, SFL, ELM, LSSVM, and GEP. In conclusion, it is recommended to use supervised learning models, specifically GRU, for precise estimation of concrete's K-eff. This approach allows engineers to save significant time and costs associated with the CSNBD test. This research contributes to the field by introducing a reliable tool for accurately predicting the K-eff of concrete, enabling efficient decision-making in various engineering applications.

• Journal of the Korea Society of Computer and Information
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• v.29 no.8
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• pp.165-170
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• 2024

This study aims to verify the accuracy of the air quality management system in Yangju City using an artificial intelligence (AI) evaluation model. The consistency and reliability of fine dust data were assessed by comparing public data from the Ministry of Environment with data from Yangju City's air quality management system. To this end, we analyzed the completeness, uniqueness, validity, consistency, accuracy, and integrity of the data. Exploratory statistical analysis was employed to compare data consistency. The results of the AI-based data quality index evaluation revealed no statistically significant differences between the two datasets. Among AI-based algorithms, the random forest model demonstrated the highest predictive accuracy, with its performance evaluated through ROC curves and AUC. Notably, the random forest model was identified as a valuable tool for optimizing the air quality management system. This study confirms that the reliability and suitability of fine dust data can be effectively assessed using AI-based model performance evaluation, contributing to the advancement of air quality management strategies.

• Clinical Endoscopy
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• v.57 no.5
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• pp.610-619
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• 2024

Background/Aims: Achalasia is a rare esophageal motility disease, for which peroral endoscopic myotomy (POEM) has emerged as a promising treatment option; however, recurrence remains a challenge. Timed barium esophagography (TBE) is a useful diagnostic tool and potential outcome predictor of achalasia. This study aimed to determine predictive tools for recurrence after POEM. Methods: This retrospective study enrolled achalasia patients who underwent POEM between January 2015 and December 2021. Patients were categorized into two groups using the 1-month post-POEM Eckardt scores and TBE: the discordant group (Eckardt score improved >50%, TBE decreased <50%) and the concordant group (both Eckardt score and TBE improved >50%). Recurrence was defined as a reincrease in the Eckardt score to more than three during follow-up. Results: Complete medical records were available in 30 patients who underwent POEM. Seventeen patients (56.7%) were classified into the discordant group, while 13 patients (43.3%) were in the concordant group. The overall recurrence rate was 11.9% at 1-year, increasing to 23.8% during the extended follow-up. The discordant group had a 6.87 fold higher recurrence rate than the concordant group (52.9% vs. 7.7%, p=0.017). Conclusions: These results strongly suggest that combining the Eckardt score with TBE can effectively predict recurrent achalasia after POEM. Patients in the discordant group had an elevated risk.

• Journal of Korean Academy of Nursing
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• v.3 no.1
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• pp.53-66
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• 1972

There is an urgent need to improve the tool predicting success or failure of academic achievement of nursing in Korea so as to identify as early as possible those students who should receive special instruction and to improve screening procedures for admission of nursing. The main purpose of this study is to identify the correlation between the grade point averages of courses learned and their psychological test characteristics in a baccalaureate nursing program. All 240 students, except freshmen, enrolled in Nursing Department of Seoul National University in the spring semester, 1972, participated in this study. All of the subjects completed the psychometric tests such as interest test, personality test and test of self-concept. Total grade point averages, grade point averages of general education subjects, of supporting science courses and of professional education subjects were used as performance criteria of the students. Through the calculation of product-moment correlation coefficients between the test scores and four grade point averages of each class and of total subjects, the following findings and recommendations were obtained. 1. There was so much variation in characteristics of interest test correlated with academic achievement of nursing students in each class. 2. Since the school objectives, curriculum and teaching strategies may affect predictive efficiency of characteristics of students'interest test, interest test must-be utilized in a homogeneous group in order to predict school achievement. 3. Characteristics of interest test positively correlated at significant level with total grade point averages of all subjects were scientific interest-biological, scientific interest-physical, and humanitarian interest. Scientific interest-physics: was the only characteristic positively correlated at significant level with total grade point averages and grade point averages of professional courses. 4. There were various patterns in characteristics of personality test correlated with school achievement of nursing students by class pattern and personality maturation as they progress toward higher classes. 5. A characteristic of personality test, responsibility, is in high positive correlation with academic achievement in the upper division of classes. 6. Responsibility was the sole personality factor positively correlated at significant level with total grade point averages and grade point averages of nursing courses in the total number of students. 7. There were very different correlation coefficients between characteristics of self-concept test and academic achievement according to the type of each class and type of courses they learned. 8. Characteristics of self-concept test positively correlated at significant level with total grade point averages and grade point averages of nursing courses of all students were physical self and row variability. Those who have positive concept on their own physical status and who are deficient in self-concept were higher in total grade point averages and grade point averages of professional courses than other students. 9. Scores of professional courses offered in freshmen and sophomore classes were in positive correlation with limited number of characteristics of psychological tests. In pursuit of a tool predicting successful academic achievement of nursing students, their G.P.A. during the junior and senior year of nursing will serve as the more reasonable criteria. 10. Junior students of this school were in higher positive correlation with many psychological factors than other classes.

• Journal of Korean Society of Forest Science
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• v.81 no.4
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• pp.372-382
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• 1992

Recreation planning is essential activity to meet changing demands and to protect the resources. The recreation opportunity specturm(ROS) system is a principal part of a recreational management planning. In this study, the basic concepts and tenets of the ROS system described and reviewed the feasibility of applying to forest recreation planning to the Korean national forest. In Korea, the forest land as a major recreation place has used without the rational planning process. The control for the laissez-faire use on the forest area, the classification of recreational opportunity settings is the most important process and then it make a useful tool for providing proper recreational opportunity and site development guidance. Opportunity settings classification can help maintain diversity and enhance protection of forest resources. It can also improve the quality of recreational experiences and the management action guidances. GIS technology using the ARC/INFO could be useful in current attempts to identify analysis areas for predictive modeling of forest recreation site planning.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.21
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• pp.9367-9373
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• 2014

In diagnosis of lung cancer, rapid distinction between small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) tumors is very important. Serum markers, including lactate dehydrogenase (LDH), C-reactive protein (CRP), carcino-embryonic antigen (CEA), neurone specific enolase (NSE) and Cyfra21-1, are reported to reflect lung cancer characteristics. In this study classification of lung tumors was made based on biomarkers (measured in 120 NSCLC and 60 SCLC patients) by setting up optimal biomarker joint models with a powerful computerized tool - gene expression programming (GEP). GEP is a learning algorithm that combines the advantages of genetic programming (GP) and genetic algorithms (GA). It specifically focuses on relationships between variables in sets of data and then builds models to explain these relationships, and has been successfully used in formula finding and function mining. As a basis for defining a GEP environment for SCLC and NSCLC prediction, three explicit predictive models were constructed. CEA and NSE are requentlyused lung cancer markers in clinical trials, CRP, LDH and Cyfra21-1 have significant meaning in lung cancer, basis on CEA and NSE we set up three GEP models-GEP 1(CEA, NSE, Cyfra21-1), GEP2 (CEA, NSE, LDH), GEP3 (CEA, NSE, CRP). The best classification result of GEP gained when CEA, NSE and Cyfra21-1 were combined: 128 of 135 subjects in the training set and 40 of 45 subjects in the test set were classified correctly, the accuracy rate is 94.8% in training set; on collection of samples for testing, the accuracy rate is 88.9%. With GEP2, the accuracy was significantly decreased by 1.5% and 6.6% in training set and test set, in GEP3 was 0.82% and 4.45% respectively. Serum Cyfra21-1 is a useful and sensitive serum biomarker in discriminating between NSCLC and SCLC. GEP modeling is a promising and excellent tool in diagnosis of lung cancer.

• Journal of Technology Innovation
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• v.29 no.4
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• pp.1-30
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• 2021

Due to the recent development of system semiconductors, technical innovation for the electric devices of the automobile industry is rapidly progressing. In particular, the electric device of automobiles is accelerating technology development competition among automobile parts makers, and the development cycle is also changing rapidly. Due to these changes, the importance of strategic planning for R&D is further strengthened. Due to the paradigm shift in the automobile industry, the Product-Technical Roadmap (P/TRM), one of the R&D strategies, analyzes technology forecasting, technology level evaluation, and technology acquisition method (Make/Collaborate/Buy) at the planning stage. The product-technical roadmap is a tool that identifies customer needs of products and technologies, selects technologies and sets development directions. However, most companies are developing the product-technical roadmap through a qualitative method that mainly relies on the technical papers, patent analysis, and expert Delphi method. In this study, empirical research was conducted through simulations that can supplement and strengthen the product-technical roadmap centered on the automobile industry by fusing Gartner's hype cycle, cumulative moving average-based data preprocessing, and deep learning (LSTM) time series analysis techniques. The empirical study presented in this paper can be used not only in the automobile industry but also in other manufacturing fields in general. In addition, from the corporate point of view, it is considered that it will become a foundation for moving forward as a leading company by providing products to the market in a timely manner through a more accurate product-technical roadmap, breaking away from the roadmap preparation method that has relied on qualitative methods.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.29 no.2
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• pp.141-158
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• 2019

Objectives: An adverse outcome pathway is a biological pathway that disturbs homeostasis and causes toxicity. It is a conceptual framework for organizing existing biological knowledge and consists of the molecular initiating event, key event, and adverse output. The AOP concept provides intuitive risk identification that can be helpful in evaluating the carcinogenicity of chemicals and in the prevention of cancer through the assessment of chemical carcinogenicity predictions. Methods: We reviewed various papers and books related to the application of AOPs for the prevention of occupational cancer. We mainly used the internet to search for the necessary research data and information, such as via Google scholar(http://scholar.google.com), ScienceDirect(www.sciencedirect.com), Scopus(www.scopus. com), NDSL(http: //www.ndsl.kr/index.do) and PubMed(http://www.ncbi.nlm.nih.gov/pubmed). The key terms searched were "adverse outcome pathway," "toxicology," "risk assessment," "human exposure," "worker," "nanoparticle," "applications," and "occupational safety and health," among others. Results: Since it focused on the current state of AOP for the prediction of toxicity from chemical exposure at work and prospects for industrial health in the context of the AOP concept, respiratory and nanomaterial hazard assessments. AOP provides an intuitive understanding of the toxicity of chemicals as a conceptual means, and it works toward accurately predicting chemical toxicity. The AOP technique has emerged as a future-oriented alternative to the existing paradigm of chemical hazard and risk assessment. AOP can be applied to the assessment of chemical carcinogenicity along with efforts to understand the effects of chronic toxic chemicals in workplaces. Based on these predictive tools, it could be possible to bring about a breakthrough in the prevention of occupational and environmental cancer. Conclusions: The AOP tool has emerged as a future-oriented alternative to the existing paradigm of chemical hazard and risk assessment and has been widely used in the field of chemical risk assessment and the evaluation of carcinogenicity at work. It will be a useful tool for prediction, and it is possible that it can help bring about a breakthrough in the prevention of occupational and environmental cancer.

• 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.