• Healthcare Informatics Research
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• v.24 no.4
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• pp.253-262
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• 2018

Objectives: We reviewed digital epidemiological studies to characterize how researchers are using digital data by topic domain, study purpose, data source, and analytic method. Methods: We reviewed research articles published within the last decade that used digital data to answer epidemiological research questions. Data were abstracted from these articles using a data collection tool that we developed. Finally, we summarized the characteristics of the digital epidemiological studies. Results: We identified six main topic domains: infectious diseases (58.7%), non-communicable diseases (29.4%), mental health and substance use (8.3%), general population behavior (4.6%), environmental, dietary, and lifestyle (4.6%), and vital status (0.9%). We identified four categories for the study purpose: description (22.9%), exploration (34.9%), explanation (27.5%), and prediction and control (14.7%). We identified eight categories for the data sources: web search query (52.3%), social media posts (31.2%), web portal posts (11.9%), webpage access logs (7.3%), images (7.3%), mobile phone network data (1.8%), global positioning system data (1.8%), and others (2.8%). Of these, 50.5% used correlation analyses, 41.3% regression analyses, 25.6% machine learning, and 19.3% descriptive analyses. Conclusions: Digital data collected for non-epidemiological purposes are being used to study health phenomena in a variety of topic domains. Digital epidemiology requires access to large datasets and advanced analytics. Ensuring open access is clearly at odds with the desire to have as little personal data as possible in these large datasets to protect privacy. Establishment of data cooperatives with restricted access may be a solution to this dilemma.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.3
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• pp.163-170
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• 2020

With the development of information and communication technologies, the growing volume of data is increasing exponentially, raising interest in big data. As technologies related to big data have developed, big data is being collected, stored, processed, analyzed, and utilized in many fields. Big data analytics in the health care sector, in particular, is receiving much attention because they can also have a huge social and economic impact. It is predicted that it will be able to use Big Data technology to analyze patients' diagnostic data and reduce the amount of money that is spent on simple hospital care. Therefore, in this thesis, patient data is analyzed to present to patients who are unable to go to the hospital or caregivers who do not have medical expertise with close care guidelines. First, the collected patient data is stored in HDFS and the data is processed and classified using R, a big data processing and analysis tool, in the Hadoop environment. Visualize to a web server using R Shiny, which is used to implement various functions of R on the web.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.10
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• pp.383-392
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• 2014

The research for coatings is one of the most popular and active research in the polymer industry. For the coatings, electronics industry, medical and optical fields are growing more important. In particular, the trend is the increasing of the technical requirements for the performance and accuracy of the coatings by the development of automotive and electronic parts. In addition, the industry has a need of more intelligent and automated system in the industry is increasing by introduction of the IoT and big data analysis based on the environmental information and the context information. In this paper, we propose an optimization model for the design of experiments based coating formulation data objects using the Internet technologies and big data analytics. In this paper, the coating formulation was calculated based on the best data analysis is based on the experimental design, modify the operator with respect to the error caused based on the coating formulation used in the actual production site data and the corrected result data. Further optimization model to correct the reference value by leveraging big data analysis and Internet of things technology only existing coating formulation is applied as the reference data using a manufacturing environment and context information retrieval in color and quality, the most important factor in maintaining and was derived. Based on data obtained from an experiment and analysis is improving the accuracy of the combination data and making it possible to give a LOT shorter working hours per data. Also the data shortens the production time due to the reduction in the delivery time per treatment and It can contribute to cost reduction or the like defect rate reduced. Further, it is possible to obtain a standard data in the manufacturing process for the various models.

• 한국노년학
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• v.39 no.2
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• pp.231-240
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• 2019

The study aimed to identify risk factors for falls as well as hospitalization status according to disease and demographic characteristics of demented inpatients by investigating the in-depth Injury Patient Surveillance System data collected by Korea Centers for Disease Control and Prevention(KCDC). Older adults over 60 years old who were diagnosed with dementia were included(n=1,732). Their data were analyzed after being assigned to either a fall group or a non-fall group. STATA was used for statistical analyses, such as frequency analysis, chi-square (χ²) test, and logistics regression. It was found that 8.0% of the demented inpatients experienced falls. According to the analysis on category of fall and non-fall group were statistically significant difference in age and Charlson Comorbidity Index(CCI) and bone density deficiency. Based on the logistic regression analysis of factors affecting falls, older adults over 80 are 2.386 times more likely to fall and based on a target with a CCI of 0, the risk of falls is 0.421 times lower, finally based on those without bone density disorder, the fall risk for those with bone density disorder was 3.581 times higher. Therefore, we expect that the important about the factors relating to falls identified in this can not only be found valuable for educating inpatients with dementia and care-givers, but also be used as reference that supports clinical professionals to make decisions on falls management for patients with dementia.

• The Korean Journal of Nuclear Medicine Technology
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• v.15 no.2
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• pp.76-82
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• 2011

Purpose: Lymphoscintigraphy is absolutely being used standard examination in lymphatic diagnosis, evaluation after treatment, and it is useful for lymphedema to plan therapy. In case of lymphoscintigraphy of lower-extremity lymphedema, it had an effect on results if patients had not pose same position on the examination of 1 min, 1 hour and 2 hours after injection. So we'll study the methods to improve confidence with minimized quantitative analysis errors by influence factors. Materials and Methods: Being used the Infinia of GE Co. we injected $^{99m}Tc$-phytate 37 MBq (1.0 mCi) 4 sylinges into 40 people's feet hypodermically from June to August 2010 in Samsung Medical Center. After we acquired images of fixed and unfixed condition, we confirmed the count values change by attenuation of soft tissue and bone according to different feet position. And we estimated 5 times increasing 2 cm of distance between $^{99m}Tc$ point source and detector each time to check counts difference according to distance change by different feet position. Finally, we compared 1 and 6 min lymphoscintigraphy images with same position to check the effect of quantitative analysis results owing to difference of amounts of movement of the $^{99m}Tc$-phytate in the lymphatic duct. Results: Percentage difference regarding error values showed minimum 2.7% and maximum 25.8% when comparing fixed and unfixed feet position of lymphoscintigraphy examination at 1 min after injection. And count values according to distance were 173,661 (2 cm), 172,095 (4 cm), 170,996 (6 cm), 167,677 (8 cm), 169,208 counts (10 cm) which distance was increased interval of 2 cm and basal value was mean 176,587 counts, and percentage difference values were not over 2.5% such as 1.27, 1.79, 2.04, 2.42, 2.35%. Also, Assessment results about amounts of movement in lymphatic duct within 6 min until scanning after injection showed minimum 0.15%, and maximum 2.3% which were amounts of movement. We can recognize that error values represent over 20% due to only attenuation of soft tissue and bone except for distance difference (2.42%) and amounts of movement in lymphatic duct (2.3%). Conclusion: It was show that if same patients posed different feet position on the examination of 1 min, 1 hour and 2 hours after injection in the lymphoscintigraphy which is evaluating lymphatic flow of patients with lymphedema and analyzing amount of intake by lymphatic system, maximum error value represented 25.8% due to attenuation of soft tissue and bone, and PASW (Predictive Analytics Software) showed that fixed and unfixed feet position was different each other. And difference of distance between detector and feet and change of count values by difference of examination beginning time after injection influence on quantitative analysis results partially. Therefore, we'll make an effort to fix feet position and make the most of fixing board in lymphoscintigraphy with quantitative analysis.

• Land and Housing Review
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• v.14 no.4
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• pp.77-93
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• 2023

The COVID-19 pandemic has brought about significant social changes through government prevention and control measures, changes in people's risk perceptions, and lifestyle changes. In response, urban inhabitants changed their behaviors significantly, including their preferences for transportation modes and urban spaces in response to government quarantine policies and concerns over the potential risk of infection in urban spaces. These changes may have long-lasting effects on urban spaces beyond the COVID-19 pandemic or they may evolve and develop new forms. Therefore, this study aims to explore the potential for urban spaces to adapt to the present and future pandemics by examining changes in urban residents' preferences in travel modes and urban space use due to the COVID-19 pandemic. This study found that overall preferences for travel modes and urban spaces significantly differ between the pre-pandemic, pandemic, and post-pandemic periods. During the pandemic, preferences for travel modes and urban spaces has decreased, except for privately owned vehicles and green spaces, which are perceived to be safe from transmission, show more favorable than others. Post-pandemic preferences for travel modes and urban spaces are less favorable than pre-pandemic with urban spaces being five times less favorable than transportation. Although green spaces and medical facilities that were positively perceived during the pandemic are expected to return to the pre-pandemic preference level, other factors of urban spaces are facing a new-normal. The findings suggest that the COVID-19 pandemic has had a significant impact on urban residents' preferences for travel modes and urban space use. Understanding these changes is crucial for developing strategies to adapt to present and future pandemics and improve urban resilience.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.111-126
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• 2017

Recommender systems based on association rule mining significantly contribute to seller's sales by reducing consumers' time to search for products that they want. Recommendations based on the frequency of transactions such as orders can effectively screen out the products that are statistically marketable among multiple products. A product with a high possibility of sales, however, can be omitted from the recommendation if it records insufficient number of transactions at the beginning of the sale. Products missing from the associated recommendations may lose the chance of exposure to consumers, which leads to a decline in the number of transactions. In turn, diminished transactions may create a vicious circle of lost opportunity to be recommended. Thus, initial sales are likely to remain stagnant for a certain period of time. Products that are susceptible to fashion or seasonality, such as clothing, may be greatly affected. This study was aimed at expanding association rules to include into the list of recommendations those products whose initial trading frequency of transactions is low despite the possibility of high sales. The particular purpose is to predict the strength of the direct connection of two unconnected items through the properties of the paths located between them. An association between two items revealed in transactions can be interpreted as the interaction between them, which can be expressed as a link in a social network whose nodes are items. The first step calculates the centralities of the nodes in the middle of the paths that indirectly connect the two nodes without direct connection. The next step identifies the number of the paths and the shortest among them. These extracts are used as independent variables in the regression analysis to predict future connection strength between the nodes. The strength of the connection between the two nodes of the model, which is defined by the number of nodes between the two nodes, is measured after a certain period of time. The regression analysis results confirm that the number of paths between the two products, the distance of the shortest path, and the number of neighboring items connected to the products are significantly related to their potential strength. This study used actual order transaction data collected for three months from February to April in 2016 from an online commerce company. To reduce the complexity of analytics as the scale of the network grows, the analysis was performed only on miscellaneous goods. Two consecutively purchased items were chosen from each customer's transactions to obtain a pair of antecedent and consequent, which secures a link needed for constituting a social network. The direction of the link was determined in the order in which the goods were purchased. Except for the last ten days of the data collection period, the social network of associated items was built for the extraction of independent variables. The model predicts the number of links to be connected in the next ten days from the explanatory variables. Of the 5,711 previously unconnected links, 611 were newly connected for the last ten days. Through experiments, the proposed model demonstrated excellent predictions. Of the 571 links that the proposed model predicts, 269 were confirmed to have been connected. This is 4.4 times more than the average of 61, which can be found without any prediction model. This study is expected to be useful regarding industries whose new products launch quickly with short life cycles, since their exposure time is critical. Also, it can be used to detect diseases that are rarely found in the early stages of medical treatment because of the low incidence of outbreaks. Since the complexity of the social networking analysis is sensitive to the number of nodes and links that make up the network, this study was conducted in a particular category of miscellaneous goods. Future research should consider that this condition may limit the opportunity to detect unexpected associations between products belonging to different categories of classification.