• Journal of the Korean Mathematical Society
• /
• v.54 no.3
• /
• pp.987-997
• /
• 2017

A deterministic model for the spread of pine wilt disease with asymptomatic carrier trees in the host pine population is designed and rigorously analyzed. We have taken four different classes for the trees, namely susceptible, exposed, asymptomatic carrier and infected, and two different classes for the vector population, namely susceptible and infected. A complete global analysis of the model is given, which reveals that the global dynamics of the disease is completely determined by the associated basic reproduction number, denoted by $\mathcal{R}_0$. If $\mathcal{R}_0$ is less than one, the disease-free equilibrium is globally asymptotically stable, and in such a case, the endemic equilibrium does not exist. If $\mathcal{R}_0$ is greater than one, the disease persists and the unique endemic equilibrium is globally asymptotically stable.

• Journal of Korea Multimedia Society
• /
• v.21 no.2
• /
• pp.271-280
• /
• 2018

Recently, big data has been growing rapidly due to the development of IT technology. Especially in the medical field, big data is utilized to provide services such as patient-customized medical care, disease management and disease prediction. In Korea, 'National Health Alarm Service' is provided by National Health Insurance Corporation. However, the prediction model has a problem of short-term prediction within 3 days and unreliability of social data used in prediction model. In order to solve these problems, this paper proposes a disease prediction model using medicine prescription data generated from actual patients. This model predicts the total number of patients and the risk of disease in each region and uses the ARIMA model for long-term predictions.

• Journal of Korean Academy of Nursing
• /
• v.40 no.4
• /
• pp.580-588
• /
• 2010

Purpose: The purpose of this study was to examine effects of decreased locomotor activity on mass, Type I and II fiber cross-sectional areas of ipsilateral and contralateral hindlimb muscles 21 days after establishing the Parkinson's disease rat model. Methods: The rat model was established by direct injection of 6-hydroxydopamine (6-OHDA, 50 ${mu}g$) into the left substantia nigra after stereotaxic surgery. Adult male Sprague-Dawley rats were assigned to one of two groups; the Parkinson's disease group (PD; n=17) and a sham group (S; n=8). Locomotor activity was assessed before and 21 days after the experiment. At 22 days after establishing the rat model, all rats were anesthetized and soleus and plantaris muscles were dissected from both ipsilateral and contralateral sides. The brain was dissected to identify dopaminergic neuronal death of substantia nigra in the PD group. Results: The PD group at 21 days after establishing the Parkinson's disease rat model showed significant decrease in locomotor activity compared with the S group. Weights and Type I and II fiber cross-sectional areas of the contralateral soleus muscle of the PD group were significantly lower than those of the S group. Conclusion: Contralateral soleus muscle atrophy occurs 21 days after establishing the Parkinson's disease rat model.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.31 no.3
• /
• pp.209-219
• /
• 2013

Epidemiological models on disease spread attempt to simulate disease transmission and associated control processes and such models contribute to greater understanding of disease spatial diffusion through of individual's contacts. The objective of this study is to develop an agent-based modeling(ABM) approach that integrates geographic information systems(GIS) to simulate the spread of FMD in spatial environment. This model considered three elements: population, time and space, and assumed that the disease would be transmitted between farms via vehicle along the roads. The model is implemented using FMD outbreak data in Andong city of South Korea in 2010 as a case study. In the model, FMD is described with the mathematical model of transmission probability, the distance of the two individuals, latent period, and other parameters. The results show that the GIS-agent based model designed for this study can be easily customized to study the spread dynamics of FMD by adjusting the disease parameters. In addition, the proposed model is used to measure the effectiveness of different control strategies to intervene the FMD spread.

• Journal of Physiology & Pathology in Korean Medicine
• /
• v.23 no.6
• /
• pp.1460-1464
• /
• 2009

In spite of many studies on statistical model for pattern identifications (PIs), little attention has been paid to the complexity of pattern diagnosis processed by oriental physicians. The aim of this study is to develop a statistical diagnostic model which discriminates four PIs using multiple indicators in stroke. Clinical data were collected from 981 stroke patients and 516 data of which PIs were agreed by two independent physicians were included. Discriminant analysis was carried out using clinical indicators such as symptoms and signs which referred to pattern diagnosis, and applied to validation samples which contained all symptoms and signs manifested. Four Fischer's linear discriminant models were derived and their accuracy and prediction rates were 93.2% and 80.43%, respectively. It is important to consider the pattern diagnosis processed by oriental physicians in developing statistical model for PIs. The discriminant model developed in this study using multiple indicators is valid, and can be used in the clinical fields.

• Journal of the Korea Society of Computer and Information
• /
• v.21 no.3
• /
• pp.65-71
• /
• 2016

To reduce the expenses for development a novel drug, systems biology has been studied actively. Target prediction, a part of systems biology, contributes to finding a new purpose for FDA(Food and Drug Administration) approved drugs and development novel drugs. In this paper, we propose a classification model for predicting novel target genes based on relation between target genes and disease related genes. After collecting known target genes from TTD(Therapeutic Target Database) and disease related genes from OMIM(Online Mendelian Inheritance in Man), we analyzed the effect of target genes on disease related genes based on PPI(Protein-Protein Interactions) network. We focused on the distinguishing characteristics between known target genes and random target genes, and used the characteristics as features for building a classifier. Because our model is constructed using information about only a disease and its known targets, the model can be applied to unusual diseases without similar drugs and diseases, while existing models for finding new drug-disease associations are based on drug-drug similarity and disease-disease similarity. We validated accuracy of the model using LOOCV of ten times and the AUCs were 0.74 on Alzheimer's disease and 0.71 on Breast cancer.

• Journal of Korean Academy of Nursing
• /
• v.31 no.3
• /
• pp.477-491
• /
• 2001

Purpose: This study was designed to construct a structural model for health promoting behavior in patients with chronic respiratory disease. A hypothetical model was developed based on the literature review. Method: Data was collected by questionnaires from 235 patients with chronic respiratory disease in a General Hospital in Seoul. Data analysis was done using SAS 6.12 for descriptive statistics and the PC-LISREL 8.13 Program for Covariance Structural Analysis. Result: The results are as follows : 1. The fit of the hypothetical model to the data was moderate. It was modified by excluding 2 path and including free parameters and 3 path to it. The modified model with path showed a good fitness to the empirical data($\chi$2=80.20, P=0.05, GFI=0.95, AGFI=0.88, NNFI=0.95, NFI=0.96, RMSR=0.01, RMSEA =0.06). 2. The perceived benefits, self-efficacy, and a plan of action were found to have significant direct effects on the health promoting behavior in patients with chronic respiratory disease. 3. The health perception, self-esteem, and activity related to affect were found to have indirect effects on the health promoting behavior in patients with chronic respiratory disease. Conclusion: The modified model of this study is considered appropriate in explaining and predicting health promoting behavior in patients with chronic respiratory disease. Therefore, it can effectively be used as a reference model for further studies and suggested direction in nursing practice.

• International Journal of Computer Science & Network Security
• /
• v.22 no.6
• /
• pp.223-229
• /
• 2022

Early detection can help prevent heart disease, which is one of the most common reasons for death. This paper provides a clinical support model for predicting cardiac disease. The model is built using two publicly available data sets. The admissibility and application of the the model are justified by a sequence of tests. Implementation of the model and testing are also discussed

• Journal of Korean Academy of Nursing
• /
• v.31 no.1
• /
• pp.107-125
• /
• 2001

This study was designed to construct a structural model for quality of life of chronic gastric disease. The hypothetical model was developed based on the literature review and Pender's health promotion model. Data were collected by questionnaires from 459 patients with chronic gastric disease in a General Hospital from July 1999 to August 2000 in Seoul. Data analysis was done with SAS 6.12 for descriptive statistics and PC-LISREL 8.13 Program for Covariance structural analysis. The results are as follows : 1. The fit of the hypothetical model to the data was moderate, thus it was modified by excluding 1 path and including free parameters and 2 path to it. The modified model with path showed a good fitness to the empirical data ($\chi$2=934.87, p<.0001, GFI=0.88, AGFI=0.83, NNFI=0.86, RMSR =0.02, RMSEA=0.07). 2. The perceived barrier, health promoting behavior, self-efficacy, and self-esteem were found to have significant direct effects on the quality of life. 3. The health concept, health perception, emotional state, and social support were found to have indirect effects on quality of life of chronic gastric disease. In conclusion, the derived model in this study is considered appropriate in explaining and predicting quality of life of chronic gastric disease. Therefore it can effectively be used as a reference model for further studies and suggested direction in nursing practice.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.9
• /
• pp.3583-3597
• /
• 2020

In this paper, a parallel deep learning model using a convolutional neural network and a dilated convolutional neural network is proposed to classify Alzheimer's disease with high accuracy in PET/CT images. The developed model consists of two pipelines, a conventional CNN pipeline, and a dilated convolution pipeline. An input image is sent through both pipelines, and at the end of both pipelines, extracted features are concatenated and used for classifying Alzheimer's disease. Complimentary abilities of both networks provide better overall accuracy than single conventional CNNs in the dataset. Moreover, instead of performing binary classification, the proposed model performs three-class classification being Alzheimer's disease, mild cognitive impairment, and normal control. Using the data received from Dong-a University, the model performs classification detecting Alzheimer's disease with an accuracy of up to 95.51%.