• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.1
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• pp.207-212
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• 2018

As a data analysis tool which is becoming popular in the Big Data era, R is rapidly expanding its user range by providing powerful statistical analysis and data visualization functions. Major advantage of R is its functional scalability based on open source, but its scale scalability is limited, resulting in performance degrades in large data processing. RHadoop, one of the extension packages to complement it, can improve data analysis performance as it supports Hadoop platform-based distributed processing of programs written in R. In this paper, we evaluate the validity of RHadoop by evaluating the performance improvement of RHadoop in real medical big data analysis. Performance evaluation of the analysis of the medical history information, which is provided by National Health Insurance Service, using R and RHadoop shows that RHadoop cluster composed of 8 data nodes can improve performance up to 8 times compared with R.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.4
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• pp.47-58
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• 2016

The purpose of this study was to select the sectoral vulnerable areas index in welfare field and analyze the socially vulnerable areas from comprehensive analysis. For this study, preceding research and theoretical background were reviewed. Through this, we selected for the analysis index and criteria, and data corresponding to the index are collected. Based on the index and criteria, the data analysis was performed in Seoul Metropolitan City selected as the spatial extent of this study. From the results of analysis, the sectoral lower rank 10% of social vulnerable areas was determined. In addition, Junggu, Yongsangu, and Seodaemungu from the comprehensive analysis of individual vulnerable areas were derived as a final vulnerable areas. In particular, Junggu was weak in all sectors; Yongsangu was in the medical sector; and Seodaemungu was poor in housing and education. Lower vulnerability index of all sectors (energy, housing, medical, transportation, and education) in 1st, 2nd, and 3rd residential areas by examining use zoning was showed. From the results of this study, we can expect time and labor saving of policy support in public sector.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.6
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• pp.1167-1174
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• 2009

In the monitoring of a patient in a sickroom, not only the physiologic and environmental data of the patient, which is automatically measured, but also the clinical data(clinical chart)of the patient, which is drew up by a doctor or nurse, are recognized as important data. However, since in the current environment of a sickroom, clinical data is collected being divided from the data that is automatically measured, the two data are used without an effective integration. This is because the integration of the two data is difficult due to their different collection times, which leads the reconstruction of clinical data to be remarkably uncertain. In order to solve these problems, a method to synchronize the continuous environmental data of a sickroom and clinical data is appearing as an important measure. In addition, the increase of use of small machines and the development of solutions based on wireless communications provide a communication platform to the developers of health care. Thus, this paper realizes a remote system for taking care of patients based on a web that uses mobile phones. That is, clinical data made by a nurse or doctor and the environmental data of a sick room comes to be collected by a collection module through a wireless sensor network. An observer can see clinical data and the environmental data of a sickroom through his/her mobile phone, integrating and storing his/her data into the database. Families of a patient can see clinical data made by hospital and the environment of the sick room of the patent through their computers or mobile phones outside the hospital. Through the system,hospital can provide better medical services to patients and their families.

• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.187-201
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• 2024

Osteoporosis is a major health issue globally, often remaining undetected until a fracture occurs. To facilitate early detection, deep learning (DL) models were developed to classify osteoporosis using abdominal computed tomography (CT) scans. This study was conducted using retrospectively collected data from 3,012 contrast-enhanced abdominal CT scans. The DL models developed in this study were constructed for using image data, demographic/clinical information, and multi-modality data, respectively. Patients were categorized into the normal, osteopenia, and osteoporosis groups based on their T-scores, obtained from dual-energy X-ray absorptiometry, into normal, osteopenia, and osteoporosis groups. The models showed high accuracy and effectiveness, with the combined data model performing the best, achieving an area under the receiver operating characteristic curve of 0.94 and an accuracy of 0.80. The image-based model also performed well, while the demographic data model had lower accuracy and effectiveness. In addition, the DL model was interpreted by gradient-weighted class activation mapping (Grad-CAM) to highlight clinically relevant features in the images, revealing the femoral neck as a common site for fractures. The study shows that DL can accurately identify osteoporosis stages from clinical data, indicating the potential of abdominal CT scans in early osteoporosis detection and reducing fracture risks with prompt treatment.