• Journal of the Korean Society of Radiology
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• v.16 no.5
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• pp.595-602
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• 2022

This study tried to suggest the most efficient learning rate for accurate and efficient automatic diagnosis of medical images for chest X-ray pneumonia images using deep learning. After setting the learning rates to 0.1, 0.01, 0.001, and 0.0001 in the Inception V3 deep learning model, respectively, deep learning modeling was performed three times. And the average accuracy and loss function value of verification modeling, and the metric of test modeling were set as performance evaluation indicators, and the performance was compared and evaluated with the average value of three times of the results obtained as a result of performing deep learning modeling. As a result of performance evaluation for deep learning verification modeling performance evaluation and test modeling metric, modeling with a learning rate of 0.001 showed the highest accuracy and excellent performance. For this reason, in this paper, it is recommended to apply a learning rate of 0.001 when classifying the presence or absence of pneumonia on chest X-ray images using a deep learning model. In addition, it was judged that when deep learning modeling through the application of the learning rate presented in this paper could play an auxiliary role in the classification of the presence or absence of pneumonia on chest X-ray images. In the future, if the study of classification for diagnosis and classification of pneumonia using deep learning continues, the contents of this thesis research can be used as basic data, and furthermore, it is expected that it will be helpful in selecting an efficient learning rate in classifying medical images using artificial intelligence.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.526-532
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• 2023

Falls are fatal accidents that occur more than 420,000 times a year worldwide. Therefore, to study patients with falls, we found the association between extrinsic injury codes and principal diagnosis S-codes of patients with falls, and developed a prediction model to predict extrinsic injury codes based on the data of principal diagnosis S-codes of patients with falls. In this study, we received two years of data from 2020 and 2021 from Institution A, located in Gangneung City, Gangwon Special Self-Governing Province, and extracted only the data from W00 to W19 of the extrinsic injury codes related to falls, and developed a prediction model using W01, W10, W13, and W18 of the extrinsic injury codes of falls, which had enough principal diagnosis S-codes to develop a prediction model. 80% of the data were categorized as training data and 20% as testing data. The model was developed using MLP (Multi-Layer Perceptron) with 6 variables (gender, age, principal diagnosis S-code, surgery, hospitalization, and alcohol consumption) in the input layer, 2 hidden layers with 64 nodes, and an output layer with 4 nodes for W01, W10, W13, and W18 exogenous damage codes using the softmax activation function. As a result of the training, the first training had an accuracy of 31.2%, but the 30th training had an accuracy of 87.5%, which confirmed the association between the fall extrinsic code and the main diagnosis S code of the fall patient.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.127-146
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• 2022

Recently, as word embedding has shown excellent performance in various tasks of deep learning-based natural language processing, researches on the advancement and application of word, sentence, and document embedding are being actively conducted. Among them, cross-language transfer, which enables semantic exchange between different languages, is growing simultaneously with the development of embedding models. Academia's interests in vector alignment are growing with the expectation that it can be applied to various embedding-based analysis. In particular, vector alignment is expected to be applied to mapping between specialized domains and generalized domains. In other words, it is expected that it will be possible to map the vocabulary of specialized fields such as R&D, medicine, and law into the space of the pre-trained language model learned with huge volume of general-purpose documents, or provide a clue for mapping vocabulary between mutually different specialized fields. However, since linear-based vector alignment which has been mainly studied in academia basically assumes statistical linearity, it tends to simplify the vector space. This essentially assumes that different types of vector spaces are geometrically similar, which yields a limitation that it causes inevitable distortion in the alignment process. To overcome this limitation, we propose a deep learning-based vector alignment methodology that effectively learns the nonlinearity of data. The proposed methodology consists of sequential learning of a skip-connected autoencoder and a regression model to align the specialized word embedding expressed in each space to the general embedding space. Finally, through the inference of the two trained models, the specialized vocabulary can be aligned in the general space. To verify the performance of the proposed methodology, an experiment was performed on a total of 77,578 documents in the field of 'health care' among national R&D tasks performed from 2011 to 2020. As a result, it was confirmed that the proposed methodology showed superior performance in terms of cosine similarity compared to the existing linear vector alignment.

• Journal of radiological science and technology
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• v.40 no.3
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• pp.469-484
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• 2017

Radiological Technologists deals with the life of a person which means professional competency is essential for the job. Nevertheless, there have been no studies in Korea that identified the job competence of radiologists. In order to define the core job competencies of Korean radiologists and to present the factor models, 147 questionnaires on job competency of radiology were analyzed using 'PASW Statistics Version 18.0' and 'AMOS Version 18.0'. The valid model consisted of five core job competencies ('Patient management', 'Health and safety', 'Operation of equipment', 'Procedures and management') and 17 sub - competencies. As a result of the factor analysis, the RMSEA value was 0.1 and the CFI, and TLI values were close to 0.9 in the measurement model of the five core job competencies. The validity analysis showed that the mean variance extraction was 0.5 or more and the conceptual reliability value was 0.7 or more, And there was a high correlation between subordinate competencies included in each subordinate competencies. The results of this study are expected to provide specific information necessary for the training and management of human resources centered on competence by clearly showing the job competence required for radiologists in Korea's health environment.

• Journal of the Korean Society of Radiology
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• v.15 no.5
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• pp.613-620
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• 2021

The purpose of this study is to propose a convolutional neural network model that can classify normal and abnormal(cardiomegaly) in chest X-ray images. The training data and test data used in this paper were used by acquiring chest X-ray images of patients diagnosed with normal and abnormal(cardiomegaly). Using the proposed deep learning model, we classified normal and abnormal(cardiomegaly) images and verified the classification performance. When using the proposed model, the classification accuracy of normal and abnormal(cardiomegaly) was 99.88%. Validation of classification performance using normal images as test data showed 95%, 100%, 90%, and 96% in accuracy, precision, recall, and F1 score. Validation of classification performance using abnormal(cardiomegaly) images as test data showed 95%, 92%, 100%, and 96% in accuracy, precision, recall, and F1 score. Our classification results show that the proposed convolutional neural network model shows very good performance in feature extraction and classification of chest X-ray images. The convolutional neural network model proposed in this paper is expected to show useful results for disease classification of chest X-ray images, and further study of CNN models are needed focusing on the features of medical images.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.9
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• pp.251-260
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• 2021

Gait analysis is one of the research fields for obtaining various information related to gait by analyzing human ambulation. It has been studied for a long time not only in the medical field but also in various academic areas such as mechanical engineering, electronic engineering, and computer engineering. Efforts have been made to determine whether there is a problem with gait through gait analysis. In this paper, as a pre-step to find out gait abnormalities, it is investigated whether it is possible to differentiate whether experiment participants wear elderly simulation suit or not by applying gait data to machine learning models for the same person. For a total of 45 participants, each gait data was collected before and after wearing the simulation suit, and a total of six machine learning models were used to learn the collected data. As a result of using an artificial neural network model to distinguish whether or not the participants wear the suit, it showed 99% accuracy. What this study suggests is that we explored the possibility of judging the presence or absence of abnormality in gait by using machine learning.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.132-144
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• 2018

3D Geo-spatial information models have been widely used in the field of Civil Engineering, Medical, Computer Graphics, Urban Management and many other. Especially, in surveying and geo-spatial field, the demand for high quality 3D geospatial information and indoor spatial information is so highly increasing. However, it is so difficult to provide a low-cost and high efficiency service to the field which demand the highest quality of 3D model, because pre-constructed spatial data are composed of different formats and storage structures according to the application purpose of each institutes. In fact, the techniques to construct a high applicable 3D geo-spatial model is very expensive to collect and analyze geo-spatial data, but most demanders of 3D geo-spatial model never want to pay the high-cost to that. This study, therefore, suggest the effective way to construct 3D geo-spatial model with low-cost of construction. In general, the effective way to reduce the cost of constructing 3D geo-spatial model as presented in previous studies is to combine the raw data obtained from point cloud observatory and UAV imagery, however this method has some limitation of usage from difficulties to approve the use of raw data because of those have been managed separately by various institutes. To solve this problem, we developed the linking & management system for unifying a high-Resolution raw geo-spatial data based on the point cloud DB and apply this system to extract the basic database from 3D geo-spatial mode for the road database registration. As a result of this study, it can be provided six contents of main entries for road registration by applying the developed system based on the point cloud DB.

• Journal of the Korean Society of Radiology
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• v.16 no.5
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• pp.559-565
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• 2022

In this study, we intend to develop a granulation tissue formation model. As a pilot experiment, a contrast agent was injected into the pylorus in 3 rats, the normal pylorus lumen size was confirmed, and a stent was placed. Stent migration was confirmed in to the duodenum within 1 week. In this experiment, stent was sutured and fixed to induce granulation tissue formation after gastrostomy under a fluoroscopic guidance. Twenty rats were divided into Healthy Group / Gastrostomy Group. After anesthesia of the Gastrostomy Group, an abdominal incision was performed, and gastrostomy was performed under a fluoroscopic guidance, and a stent was placed into the pylorus. In order to prevent stent migration due to peristalsis, suture between the pylorus and the proximal end of the stent was performed. Postoperative behavior and weight changes were monitored daily. Four weeks after surgery, gastrointestinal fluoroscopy imaging was performed and rats were sacrifices. To evaluate the degree of granulation formation, the stent was sectioned transversely. Gastrostomy group was statistically significantly higher than Healthy Group in granulation area ratio (all p<.001). In conclusion, it is considered that the level of tissue overgrowth formation for preclinical evaluation of the pylorus stricture model through gastrostomy is appropriate as a research evaluation tool.

• 한국노년학
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• v.30 no.4
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• pp.1129-1143
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• 2010

The purposes of this study were to investigate the major determinants influencing on health promoting behaviors(HPB) of the elderly living in Seoul. The conceptual framework of the study was Pender's health promoting model and the ecological perspectives. The study was conducted with 495 elderly persons whom 60 years old. For the analysis of data, descriptive statistics and hierarchical regression were used for the statistical analysis with SPSS program. The results were as following: 1) The mean score of the HPB was 3.11(SD=0.41). 2) Hierarchical regression analysis found that ModelIV accounted for 55.7% of the variance in HPB. 3) The Major determinants on HPB among the elderly persons were prior related perceived benefits of action, social support, perceived self-efficacy, community environment, perceived health status, education, and age. In conclusions, first, we should develop to various levels of educational and supportive programs for the HPB among the elderly persons. Second, we should examine more with environment, the accessibility to senior welfare agencies. Third, we should be organized the self-help groups for the elderly persons to improve health promoting behaviors. Fourth, the government should established more secure environment for the HPB, and find better solutions that are provided by various social welfare agencies connected with the coordination of the services in the local communities. Finally, we should develop professional education training programs of the HPB for the practitioners in the field of Gerontological Social Work.

• Journal of Family Resource Management and Policy Review
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• v.16 no.2
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• pp.123-143
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• 2012

This study, based on a health belief model, examines how baby boomers perceive health and how they are financially preparing for future medical expenses. In addition, the study analyzes which factors influence baby boomers' preparation behaviors for future medical expenses and their perceived sufficiency of the preparation for medical expenses. Through such activities, this study examines baby boomers' current preparation status for future medical expenses, and based on this outcome, will turn the attention of individuals and society toward becoming more concerned with health and increasing health expectancy. For this study, an online survey was conducted targeted at men and women who were born between 1955 and 1963 and live nationwide, and its resultant data were collected. After conducting a 15-day survey in November 2011, a total of 418 questionnaire responses were used for the final analysis. The major findings of this study and their implications are as follows: First, baby boomers' health beliefs and their perceptions of health identified by subjective health conditions were very positive. Second, while there were some partial differences in the influencing factors, health beliefs and perceived health influenced the sufficiency of future medical expenses in the three groups, which were segmented according to how they prepare for future medical expenses-insurance-based, pension-based, and insufficiently prepared groups. Third, the baby boomers selected the national health insurance as the primary means of preparing for post-retirement medical expenses, and backed it up with private health insurance or the national pension. In addition, when baby boomers' perceived sufficiency of future medical expenses were examined, 57.6% of the respondents expressed that their old-age medical expenses were not sufficient. Fourth, in terms of baby boomers' preparation behaviors for future medical expenses, it was revealed that as one recognizes old-age health more seriously, he/she has a higher chance of using insurance and lower chance of using a pension to prepare for medical expenses. Fifth, regarding baby boomers' sufficiency of preparations for future medical expenses, economic factors such as total assets, the sufficiency of retirement assets, and the number of insurance policies, as well as health perceptions, including health beliefs and subjective health conditions, were important influencing factors.