• Journal of Korean Academy of Nursing
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• v.52 no.5
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• pp.535-549
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• 2022

Purpose: The study aimed to design and develop an automated personalized self-care (APSC) program for patients with type 2 diabetes mellitus. The secondary aim was to present a clinical protocol as a mixed-method research to test the program effects. Methods: The APSC program was developed in the order of analysis, design, implementation, and evaluation according to the software development life cycle, and was guided by the self-regulatory theory. The content validity, heuristics, and usability of the program were verified by experts and patients with type 2 diabetes mellitus. Results: The APSC program was developed based on goal setting, education, monitoring, and feedback components corresponding to the phases of forethought, performance/volitional control, and self-reflection of self-regulatory theory. Using the mobile application, the participants are able to learn from educational materials, monitor their health behaviors, receive weekly-automated personalized goals and feedback messages, and use an automated conversation system to solve the problems related to self-care. The ongoing two-year study utilizes a mixed method design, with 180 patients having type 2 diabetes mellitus randomized to receive either the intervention or usual care. The participants will be reviewed for self-care self-efficacy, health behaviors, and health outcomes at 6, 12, 18, and 24 months. Participants in the intervention group will be interviewed about their experiences. Conclusion: The APSC program can serve as an effective tool for facilitating diabetes health behaviors by improving patients' self-care self-efficacy and self-regulation for self-care. However, the clinical effectiveness of this program requires further investigation.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.13-18
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• 2008

In these days, wearable and mobile physiological sensing devices have been studied according to the increasing interest on the healthy and wellbeing life. However, these sensing devices display just the sensing results, such as heart rate, skin temperature, and its daily records. In this work, we propose the novel type of mobile physiological sensing device which deliver the user comfortable grabbing feeling. In addition, we indicate the personalized physiological signal analysis result which be concluded by the different analysis results according to the person to person. In order to verify this sensing device, we collect the data set from 4 different users during a week and measure the physiological signal such as heart rate, hand temperature, and skin conductance. And we observe the result how the analysis results shows the difference between the users. We expect that this work can be applied in the various health care applications in the near future.

• Journal of Gastric Cancer
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• v.17 no.4
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• pp.342-353
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• 2017

Purpose: This study evaluated the changes in nutritional status based on quality of life (QoL) item-level analysis to determine whether individual QoL responses might facilitate personal clinical impact. Materials and Methods: This study retrospectively evaluated QoL data obtained by the European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire-Core 30 (QLQ-C30) and Quality of Life Questionnaire-Stomach (QLQ-STO22) as well as metabolic-nutritional data obtained by bioelectrical impedance analysis and blood tests. Patients were assessed preoperatively and at the 5-year follow-up. QoL was analyzed at the level of the constituent items. The patients were categorized into vulnerable and non-vulnerable QoL groups for each scale based on their responses to the QoL items and changes in the metabolic-nutritional indices were compared. Results: Multiple shortcomings in the metabolic-nutritional indices were observed in the vulnerable groups for nausea/vomiting (waist-hip ratio, degree of obesity), dyspnea (hemoglobin, iron), constipation (body fat mass, percent body fat), dysphagia (body fat mass, percent body fat), reflux (body weight, hemoglobin), dry mouth (percent body fat, waist-hip ratio), and taste (body weight, total body water, soft lean mass, body fat mass). The shortcomings in a single index were observed in the vulnerable groups for emotional functioning and pain (EORTC QLQ-C30) and for eating restrictions (EORTC QLQ-STO22). Conclusions: Long-term postoperative QoL deterioration in emotional functioning, nausea/vomiting, pain, dyspnea, constipation, dysphagia, reflux, eating restrictions, dry mouth, and taste were associated with nutritional shortcomings. QoL item-level analysis, instead of scale-level analysis, may help to facilitate personalized treatment for individual QoL respondents.

• KIISE Transactions on Computing Practices
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• v.21 no.7
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• pp.482-487
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• 2015

This paper presents the results and the process of extracting correlations between events of prescriptions and examinations using graph-modeling and node centrality measures on a medical dataset of 11,938 patients with diabetes mellitus. As the data is stored in relational form, RDB2Graph framework was used to construct effective graph models from the data. Personalized PageRank was applied to analyze correlation between prescriptions and examinations of the patients. Two graph models were constructed: one that models medical events by each patient and another that considers the time gap between medical events. The results of the correlation analysis confirm current medical knowledge. The paper demonstrates some of the note-worthy findings to show the effectiveness of the method used in the current analysis.

• Journal of the Korea Society of Computer and Information
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• v.26 no.10
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• pp.117-124
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• 2021

In this paper, we propose CANVAS (Creative ANalytics enVironment And System), an analytics system of the National Research Data Platform (DataON). CANVAS is a personalized analytics cloud service for researchers who need computing resources and tools for research data analysis. CANVAS is designed in consideration of scalability based on micro-services architecture and was built on top of open-source software such as eGovernment Standard framework (Spring framework), Kubernetes, and JupyterLab. The built system provides personalized analytics environments to multiple users, enabling high-speed and large-capacity analysis by utilizing high-performance cloud infrastructure (CPU/GPU). More specifically, modeling and processing data is possible in JupyterLab or GUI workflow environment. Since CANVAS shares data with DataON, the research data registered by users or downloaded data can be directly processed in the CANVAS. As a result, CANVAS enhances the convenience of data analysis for users in DataON and contributes to the sharing and utilization of research data.

• The Journal of the Korea Contents Association
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• v.8 no.3
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• pp.1-7
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• 2008

Due to the development of ubiquitous computing, a wide variety of information is being produced and distributed rapidly in digital form. In this excess of information, it is not easy for users to search and find their desired information in short time. In this paper, we propose the personalized item recommendation using the image based filtering. This research uses the image based filtering which is extracting the feature from the image data that a user is interested in, in order to improve the superficial problem of content analysis. We evaluate the performance of the proposed method and it is compared with the performance of previous studies of the content based filtering and the collaborative filtering in the MovieLens dataset. And the results have shown that the proposed method significantly outperforms the previous methods.

• ETRI Journal
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• v.35 no.6
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• pp.1105-1114
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• 2013

Owing to the large number of video programs available, a method for accessing preferred videos efficiently through personalized video summaries and clips is needed. The automatic recognition of user states when viewing a video is essential for extracting meaningful video segments. Although there have been many studies on emotion recognition using various user responses, electroencephalogram (EEG)-based research on preference recognition of videos is at its very early stages. This paper proposes classification models based on linear and nonlinear classifiers using EEG features of band power (BP) values and asymmetry scores for four preference classes. As a result, the quadratic-discriminant-analysis-based model using BP features achieves a classification accuracy of 97.39% (${\pm}0.73%$), and the models based on the other nonlinear classifiers using the BP features achieve an accuracy of over 96%, which is superior to that of previous work only for binary preference classification. The result proves that the proposed approach is sufficient for employment in personalized video segmentation with high accuracy and classification power.

• Journal of The Korean Association of Information Education
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• v.7 no.2
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• pp.189-197
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• 2003

WBI(Web Based Instruction) has been confined to some course due to a burden of development of instruction materials. In this paper, we implemented a personalized instruction and learning system for Word Processor based on Internet by using WBI. Compared to the traditional instruction and learning method for Word Processor Education, the proposed method induce students to take an interest in the learning and make it possible to do student oriented instruction and learning due to the selection of specific contents according to student's ability and his/her learning step. And this system can evaluate the learning rate on the spot by using personalized homework and maximize learning effect by using feedback.

• The KIPS Transactions:PartD
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• v.18D no.6
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• pp.423-432
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• 2011

In this paper, we introduce XOnto-Apriori algorithm which is an extension of the Onto-Apriori algorithm. The extended algorithm is designed to improve the conventional algorithm's problem of comparing only identifiers of transaction items by reasoning transaction properties of the items which belong in the same category. We show how the mining algorithm works with a smartphone application recommender system based on our extended algorithm to clearly describe the procedures providing personalized recommendations. Further, our simulation results validate our analysis on the algorithm overhead, precision, and recall.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.11
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• pp.3937-3954
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• 2014

With the increasing electricity consumption and the wide application of renewable energy sources, energy auction attracts a lot of attention due to its economic benefits. Many schemes have been proposed to support energy auction in smart grid. However, few of them can achieve range query, ranked search and personalized search. In this paper, we propose an efficient multi-keyword range query (EMRQ) scheme, which can support range query, ranked search and personalized search simultaneously. Based on the homomorphic Paillier cryptosystem, we use two super-increasing sequences to aggregate multidimensional keywords. The first one is used to aggregate one buyer's or seller's multidimensional keywords to an aggregated number. The second one is used to create a summary number by aggregating the aggregated numbers of all sellers. As a result, the comparison between the keywords of all sellers and those of one buyer can be achieved with only one calculation. Security analysis demonstrates that EMRQ can achieve confidentiality of keywords, authentication, data integrity and query privacy. Extensive experiments show that EMRQ is more efficient compared with the scheme in [3] in terms of computation and communication overhead.