• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1183-1191
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• 2016

According to WHO (World Health Organization), Korea ranked 1st place for TB mortality rate among OECD countries. In order to improve the situation, several administrative policies have been suggested and their efforts start showing some improvement. Meanwhile, those policies must be supported by solid scientific evidences by conducting appropriate statistical analyses. In particular, incidence and mortality rates of respiratory infectious disease such as TB must be analyzed considering their geographical characteristics. In this paper, we analyzed TB mortality rates in Korea from 2000 to 2011 using one of bayesian spatio-temporal models, which is implemented as R package (R-INLA).

• Korean Journal of Agricultural and Forest Meteorology
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• v.3 no.1
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• pp.16-21
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• 2001

Regression models were obtained on the base of the correlation between Phytophthora blight incidence in red pepper and the microclimate data obtained from automated weather station (AWS) during 1997 and 1998. A computer program (PEPBLIGHT) was constructed based on the model that the R2 value is highest among regression models. This computer program uses the microclimate data from more than one AWS through the common dialogue box easy and it is able provide disease forecasting information. In addition, it could be applied far other diseases and converts the microclimate data of AWS to the input data for Statical Analysis System (SAS). PEPBLIGHT was first developed for the forecasting computer system of red pepper blight in Korea. PEPBLIGHT is operated on the MS Windows, so that it is easy to use.

• International Journal of Contents
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• v.17 no.4
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• pp.1-15
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• 2021

Named entity recognition (NER) is a crucial task for NLP, which aims to extract information from texts. To build NER systems, deep learning (DL) models are learned with dictionary features by mapping each word in the dataset to dictionary features and generating a unique index. However, this technique might generate noisy labels, which pose significant challenges for the NER task. In this paper, we proposed DL-dictionary features, and evaluated them on two datasets, including the OntoNotes 5.0 dataset and our new infectious disease outbreak dataset named GFID. We used (1) a Bidirectional Long Short-Term Memory (BiLSTM) character and (2) pre-trained embedding to concatenate with (3) our proposed features, named the Convolutional Neural Network (CNN), BiLSTM, and self-attention dictionaries, respectively. The combined features (1-3) were fed through BiLSTM - Conditional Random Field (CRF) to predict named entity classes as outputs. We compared these outputs with other predictions of the BiLSTM character, pre-trained embedding, and dictionary features from previous research, which used the exact matching and partial matching dictionary technique. The findings showed that the model employing our dictionary features outperformed other models that used existing dictionary features. We also computed the F1 score with the GFID dataset to apply this technique to extract medical or healthcare information.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.32-42
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• 2023

There are serious problems worldwide, such as a pandemic due to an unprecedented infection caused by COVID-19. On previous approaches, they invented medical vaccines and preemptive testing tools for medical engineering. However, it is difficult to access poor medical systems and medical institutions due to disparities between countries and regions. In advanced nations, the damage was even greater due to high medical and examination costs because they did not go to the hospital. Therefore, from a software engineering-based perspective, we propose a learning model for determining coronavirus infection through symptom data-based software prediction models and tools. After a comparative analysis of various models (decision tree, Naive Bayes, KNN, multi-perceptron neural network), we decide to choose an appropriate decision tree model. Due to a lack of data, additional survey data and overseas symptom data are applied and built into the judgment model. To protect from thiswe also adapt human normalization approach with traditional Korean medicin approach. We expect to be possible to determine coronavirus, flu, allergy, and cold without medical examination and diagnosis tools through data collection and analysis by applying decision trees.

• Journal of Broadcast Engineering
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• v.28 no.3
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• pp.275-284
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• 2023

Gene expression data can be utilized in various studies, including the prediction of disease prognosis. However, there are challenges associated with collecting enough data due to cost constraints. In this paper, we propose a gene expression data generation model based on Conditional Variational Autoencoder. Our results demonstrate that the proposed model generates synthetic data with superior quality compared to two other state-of-the-art models for gene expression data generation, namely the Wasserstein Generative Adversarial Network with Gradient Penalty based model and the structured data generation models CTGAN and TVAE.

• Korean Journal of Plant Resources
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• v.36 no.3
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• pp.198-206
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• 2023

The effectiveness of the extracts of Alnus japonica and Portulaca oleracea, which are effective in improving alcohol-induced liver damage, was confirmed using acute and chronic alcoholic liver injury animal models. In the acute alcoholic liver injury model, dieting Alnus japonica and Portulaca oleracea complex (ALPOC) at a dose of 50 mg/kg showed no significant change in liver or body weight, while measured plasma ALT activity to be deficient (28.12 U/ml) compared to the alcohol intake group (42.5 U/ml), and confirmed that restored it to an average level. It showed an improvement of 34.9% compared to the alcohol intake group. AST activity confirmed that it showed a very effective liver protection activity by showing a gain of 12.6%. The chronic alcoholic liver damage animal model demonstrated that ALT showed an improvement effect of 25%, and AST showed an effect similar to that of the positive control group, Hovenia extract. In addition, through H&E staining analysis, observed that the ALPOC improved the necrosis and bleeding of the liver. And the ALPOC group showed intense antioxidant activity of 127% or more compared to the alcohol intake group, and this was confirmed to have a very high activity, which is more than 20% higher than that of the hovenia fruit extract.

• BMB Reports
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• v.56 no.10
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• pp.537-544
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• 2023

The share of the population that is aging is growing rapidly. In an aging society, technologies and interventions that delay the aging process are of great interest. Dietary restriction (DR) is the most reproducible and effective nutritional intervention tested to date for delaying the aging process and prolonging the health span in animal models. Preventive effects of DR on age-related diseases have also been reported in human. In addition, highly conserved signaling pathways from small animal models to human mediate the effects of DR. Recent evidence has shown that the immune system is closely related to the effects of DR, and functions as a major mechanism of DR in healthy aging. This review discusses the effects of DR in delaying aging and preventing age-related diseases in animal, including human, and introduces the molecular mechanisms that mediate these effects. In addition, it reports scientific findings on the relationship between the immune system and DR-induced longevity. The review highlights the role of immunity as a potential mediator of the effects of DR on longevity, and provides insights into healthy aging in human.

• Journal of Medicine and Life Science
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• v.20 no.4
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• pp.141-157
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• 2023

Stroke is a leading cause of disability and death. The condition requires prompt diagnosis and treatment. The quality of care provided to patients with stroke can vary depending on the availability of medical resources, which in turn, can affect prognosis. Recently, there has been growing interest in using machine learning (ML) to support stroke diagnosis and treatment decisions based on large medical data sets. Current ML applications in stroke care can be divided into two categories: analysis of neuroimaging data and clinical information-based predictive models. Using ML to analyze neuroimaging data can increase the efficiency and accuracy of diagnoses. Commercial software that uses ML algorithms is already being used in the medical field. Additionally, the accuracy of predictive ML models is improving with the integration of radiomics and clinical data. is expected to be important for improving the quality of care for patients with stroke.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.1
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• pp.1-6
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• 2023

This study developed models using decision forest, support vector machine, and logistic regression methods to predict and prevent suicidal ideation among Korean adolescents. The study sample consisted of 51,407 individuals after removing missing data from the raw data of the 18th (2022) Youth Health Behavior Survey conducted by the Korea Centers for Disease Control and Prevention. Analysis was performed using the MS Azure program with Two-Class Decision Forest, Two-Class Support Vector Machine, and Two-Class Logistic Regression. The results of the study showed that the decision forest model achieved an accuracy of 84.8% and an F1-score of 36.7%. The support vector machine model achieved an accuracy of 86.3% and an F1-score of 24.5%. The logistic regression model achieved an accuracy of 87.2% and an F1-score of 40.1%. Applying the logistic regression model with SMOTE to address data imbalance resulted in an accuracy of 81.7% and an F1-score of 57.7%. Although the accuracy slightly decreased, the recall, precision, and F1-score improved, demonstrating excellent performance. These findings have significant implications for the development of prediction models for suicidal ideation among Korean adolescents and can contribute to the prevention and improvement of youth suicide.

• BMB Reports
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• v.57 no.3
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• pp.143-148
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• 2024

Pulmonary fibrosis is a serious lung disease that occurs predominantly in men. Genistein is an important natural soybean-derived phytoestrogen that affects various biological functions, such as cell migration and fibrosis. However, the antifibrotic effects of genistein on pulmonary fibrosis are largely unknown. The antifibrotic effects of genistein were evaluated using in vitro and in vivo models of lung fibrosis. Proteomic data were analyzed using nano-LC-ESI-MS/MS. Genistein significantly reduced transforming growth factor (TGF)-β1-induced expression of collagen type I and α-smooth muscle actin (SMA) in MRC-5 cells and primary fibroblasts from patients with idiopathic pulmonary fibrosis (IPF). Genistein also reduced TGF-β1-induced expression of p-Smad2/3 and p-p38 MAPK in fibroblast models. Comprehensive protein analysis confirmed that genistein exerted an anti-fibrotic effect by regulating various molecular mechanisms, such as unfolded protein response, epithelial mesenchymal transition (EMT), mammalian target of rapamycin complex 1 (mTORC1) signaling, cell death, and several metabolic pathways. Genistein was also found to decrease hydroxyproline levels in the lungs of BLM-treated mice. Genistein exerted an anti-fibrotic effect by preventing fibroblast activation, suggesting that genistein could be developed as a pharmacological agent for the prevention and treatment of pulmonary fibrosis.