• KIISE Transactions on Computing Practices
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• v.21 no.7
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• pp.464-469
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• 2015

Long-term electrocardiogram data can be acquired by linking a Holter monitor to a mobile phone. However, most systems are designed to detect arrhythmia through heartbeat classification, and not just for supporting clinical decisions. In this paper, we propose an Abstracting algorithm, and introduce an analogous pateint search system using this algorithm. An analogous patient searcher summarizes each patient's typical pattern using the results of heartbeat, which can greatly simplify clinical activity. It helps to find patients with similar arrhythmia patterns, which can help in contributing to diagnostic clues. We have simulated these processes on data from the MIT-BIH arrhythmia database. As a result, the Abstracting algorithm provided a typical pattern to assist in reaching rapid clinical decisions for 64% of the patients. On an average, typical patterns and results generated by the abstracting algorithm summarized the results of heartbeat classification by 98.01%.

• Annual Conference of KIPS
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• 2023.11a
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• pp.418-421
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• 2023

본 논문은 Word2Vec를 이용하여 한국어 증상 기반 질병 예측 모델을 제시한다. 아산병원 질환 백과의 크롤링 데이터를 세 가지 형식으로 나누어, 모델에 알맞은 데이터 형식을 찾고 모델에 적용한다. 가장 모델에 맞는 데이터 형식은 증상별 질병과 질병별 증상을 합친 경우이다. 데이터의 양을 늘려 임베딩 스페이스를 넓혔고, 가장 중요한 증상과 질병의 유사도도 정확하게 출력되었다. 이는 유사도가 높은 질병과 증상들이 제대로 학습이 되었다는 것을 알 수 있다. 이렇게 만들어진 예측 모델에 positive 증상을 입력하면 유사도가 향상되고, negative에 입력하면 하락하는 결과를 확인했다. 따라서 환자의 증상을 positive에 넣으면, 그 증상을 가진 질병이 가까워지는 반면, 환자의 증상이 아닌 증상을 negative에 넣으면, 환자에게 맞지 않는 질병이 멀어진다. 그러므로 환자의 상태에 맞는 질병을 유추해, 의사나 환자가 증상에 대한 질병을 알고 싶을 때 또는 검색에 유용하게 사용할 수 있다. 더불어, 질병의 진료과 데이터를 추가하여, 환자에게 맞는 진료과를 찾는 데도 도움을 줄 수 있다.

• Journal of Internet of Things and Convergence
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• v.2 no.4
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• pp.15-20
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• 2016

This paper describes the construction of knowledge data retrieval management system based on medical image CT. The developed system is aimed to improve the efficiency of the hospital by reading the medical images using the intelligent retrieval technology and diagnosing the patient 's disease name. In this study, the medical image DICOM file of PACS is read, the image is processed, and feature values are extracted and stored in the database. We have implemented a system that retrieves similarity by comparing new CT images required for medical treatment with the feature values of other CTs stored in the database. After converting 100 CT dicom provided for academic research into JPEG files, Code Book Library was constructed using SIFT, CS-LBP and K-Mean Clustering algorithms. Through the database optimization, the similarity of the new CT image to the existing data is searched and the result is confirmed, so that it can be utilized for the diagnosis and diagnosis of the patient.

• Journal of Korea Multimedia Society
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• v.8 no.8
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• pp.1032-1041
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• 2005

Now a days, PACS and the other image sharing systems use only high-level metadata for hospital to retrieve images. So if you want to retrieve some images, you have to know exact information about the patient. In this paper, we developed a Image Retrieval System based on MPEG-7 to retrieve medical images more efficiently. This system offers keyword retrieval using high-level metadata based of DICOM and similarity retrieval using low-level metadata based on MFEG-7. And we integrated high-level metadata and low-level metadata to retrieve medical images more exactly.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.3
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• pp.537-542
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• 2015

SNOMED CT, which is a standard clinical terminology, imposes an ambiguity problem of terminology selections caused by its huge expressive power and structural complexity. It is very difficult to distinguish similar terms and to select an appropriate term among them within short consultation hours. This paper analyzes the ambiguity problem and proposes a novel scheme for comparative search of similar terms. The proposed scheme provides a differential view of similar terms by defining a "is-not-a" relationship based on the hierarchical structure of the concepts. The result of this work improves the utilization of SNOMED CT such that medical officers can efficiently select an appropriate term among similar terms which represents patient's status adequately.

• Journal of Nutrition and Health
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• v.43 no.2
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• pp.189-196
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• 2010

Malnutrition has been associated with higher hospital costs, mortality, rates of complications and longer length of hospital stay. Several nutritional screening tools have been developed to identify patients with malnutrition risk. However, many of those require much time and labor to administer and may not be applicable to a Korean population. Therefore, the aim of this study was to develop nutritional screening tool for Korean inpatients. Then we compare nutritional screening tools that developed and previously described. Seven hundred sixty-four patients at hospital admission were screened nutritional status and classified as well nourished, malnutrition stage 1 or stage 2 by the KNNRS (Kyunghee Neo Nutrition Risk Screening), PG-SGA (Patient-Generated Subjective Global Assessment) and NRS-2002 (Nutritional Risk Screening-2002). The KNNRS, PG-SGA and NRS-2002 respectively classified 28.7%, 51.3%, 48.5% of patients as malnourished status. Compared to the PG-SGA, the KNNRS had sensitivity 60.7% (95% CI 54.2-67.0) and specificity 81.2% (95% CI 75.3-85.2). Agreement was fair between KNNRS and PG-SGA (k = 0.34). Compared to the NRS-2002, the KNNRS had sensitivity 57.8% (95% CI 53.4-60.9) and specificity 64.4% (95% CI 60.2-69.8). Agreement was poor between KNNRS and NRS-2002 (k = 0.18). These result should include that the KNNRS and PGSGA have clinical relevance and fair concordance. However the rate of malnourished patients by KNNRS were less than by PG-SGA. For more effectivity of nutritional screening and management, the criteria of KNNRS would be better revised.

• Proceedings of the Korea Multimedia Society Conference
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• 2000.11a
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• pp.73-76
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• 2000

본 논문은 의료 영상 검색 시스템에서 뇌 MRI 이미지 데이터베이스에서 사용자의 질의를 만족하는 질의 결과 집합에 대한 시각화 방안을 제안한다. 한 환자의 뇌 MRI 이미지를 검색 결과로 제시할 경우 종류별, 방향별로 다양하고 여러 환자의 경우에는 그 양이 더욱 방대해진다. 이러한 뇌 MRI 이미지를 공간 제약적인 화면에 표현하는데는 한계가 있다. 따라서 본 논문에서는 질의 결과를 제시할 대 유사도가 높은 순서로 나열하고, 사용자 요구에 따른 관련 이미지를 종류와 방향별로 제시하여 이미지 조작을 가능하게 한다. 도한, 제시된 뇌 MRI 이미지를 효율적으로 브라우징할 수 있도록 3차원 블록 메타포어를 이용한 시각화 인터페이스를 통하여 공간 활용도의 향상과 사용자 인터페이스의 편의성 및 인지도를 증진한다.

• Journal of Nutrition and Health
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• v.52 no.4
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• pp.332-341
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• 2019

Purpose: Poor nutrition in hospitalized patients is closely linked to an increased risk of infection, which can result in complications affecting mortality, as well as increased length of hospital stay and hospital costs. Therefore, adequate nutritional support is essential to manage the nutritional risk status of patients. Nutritional support needs to be preceded by nutrition screening, in which accuracy is crucial, particularly for the initial screening. To perform initial nutrition screening of hospitalized patients, we used the Catholic Kwandong University (CKU) Nutritional Risk Screening (CKUNRS) tool, originally developed at CKU Hospital. To validate CKUNRS against the Patient-Generated Subjective Global Assessment (PG-SGA) tool, which is considered the gold standard for nutritional risk screening, results from both tools were compared. Methods: Nutritional status was evaluated in 686 adult patients admitted to CKU Hospital from May 1 to July 31, 2018 using both CKUNRS and PG-SGA. Collected data were analyzed, and the results compared, to validate CKUNRS as a nutrition screening tool. Results: The comparison of CKUNRS and PG-SGA revealed that the prevalence of nutritional risk on admission was 15.6% (n = 107) with CKUNRS and 44.6% (n = 306) with PG-SGA. The sensitivity and specificity of CKUNRS to evaluate nutritional risk status were 98.7% (96.8 ~ 99.5) and 33.3% (28.1 ~ 39.0), respectively. Thus, the sensitivity was higher, but the specificity lower compared with PG-SGA. Cohen's kappa coefficient was 0.34, indicating valid agreement between the two tools. Conclusion: This study found concordance between CKUNRS and PG-SGA. However, the prevalence of nutritional risk in hospitalized patients was higher when determined by CKUNRS, compared with that by PG-SGA. Accordingly, CKUNRS needs further modification and improvement in terms of screening criteria to promote more effective nutritional support for patients who have been admitted for inpatient care.

• Journal of Korea Entertainment Industry Association
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• v.14 no.5
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• pp.255-266
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• 2020

This study conducted a systematic literature review on nursing students to find out the types and composition of patient safety-related education programs. The research was conducted systematically using PRISMA. The patient was a nursing college student, and the intervention was patient safety education. The outcome was either numerical or descriptive reports of the results measured using the tool. The types of research were carried out including randomized design, quasi-experimental design, one-group design, survey and qualitative research. The paper was searched through Medline, Embases, CINAHL, DBpia, Riss and KISS. A total of 2,468 papers were searched in the search results, and nine papers were used for analysis as a result of extracting data according to PICO. The characteristics of patient safety education of nursing college students were classified according to the teaching method, period, result variables and measurement tools. As a result, patient safety education consisted of a variety of subjects, and was being taught through lectures, clinical practice, laboratories, and simulations. The period of education also varied. The resulting variables were mainly checked for knowledge, attitude, and skill, and the tools used to measure them varied. Patient safety education for nursing college students was conducted through various topics, methods and periods at home and abroad. Therefore, continuous research is needed to establish consistent and integrated patient safety education in educational institutions and working institutions to produce nursing personnel with patient safety capabilities.

• Journal of the Korea Convergence Society
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• v.10 no.8
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• pp.15-20
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• 2019

Music therapy has shown many benefits in the treatment of disabled children and the mind. Today's music therapy system is a situation where no specific treatment system has been built. In order for the music therapist to make an accurate treatment, various music therapy cases and treatment history data must be analyzed. Although the most appropriate treatment is given to the client or patient, in reality a number of difficulties are followed due to several factors. In this paper, we propose a music therapy knowledge management model which convergence the existing therapy data and text mining technology. By using the proposed model, similar cases can be searched and accurate and effective treatment can be made for the patient or the client based on specific and reliable data related to the patient. This can be expected to bring out the original purpose of the music therapy and its effect to the maximum, and is expected to be useful for treating more patients.