• 건강소식
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• v.38 no.8
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• pp.14-15
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• 2014

인터넷 검색창에 '수족냉증'을 쳐보면 의외의 결과에 놀라게 된다. 보통 여성들이 출산 후 산후조리를 소홀히 해 손과 발이 시린 수족냉증을 호소한다고 알려져 있지만, 사실 수족냉증은 10대 청소년부터 혈기왕성한 대학생, 직장 남성까지 매우 다양한 연령대의 환자들이 고통받고 있는 생활형 질병이다.

• Journal of Convergence Society for SMB
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• v.6 no.1
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• pp.33-38
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• 2016

Disease of patient who visited the hospital can cause different symptoms of the disease, depending on the environment and lifestyle. Recent medical services offered in patients has changed in the environment that can be selected for treatment by analyzing the patient according to the disease symptoms. In this paper, we propose an efficient method to manage disease control because the treatment method may change at any patients suffering from the disease according to the patient conditions by grouping the different treatments to patients for disease information. The proposed scheme has a feature that can be ingested by the patient big disease information, as well as to improve the treatment efficiency of the medical treatment the increase patient satisfaction. The proposed sheme can handle big data by clustering of disease information for patients suffering from diseases such as patient consent small groups. In addition, the proposed scheme has the advantage that can be conveniently accessed via a particular keyword, the treatment method according to patient disease information. The experimental results, the proposed method has been improved by 23% in terms of efficiency compared to conventional techniques, disease management time is gained 11.3% improved results. Medical service user satisfaction seen from the survey is to obtain a high 31.5% results.

• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.31-39
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• 2014

Seasonal influenza epidemics cause 3 to 5 millions severe illness and 250,000 to 500,000 deaths worldwide each year. To prepare better controls on severe influenza epidemics, many studies have been proposed to achieve near real-time surveillance of the spread of influenza. Korea CDC publishes clinical data of influenza epidemics on a weekly basis typically with a 1-2-week reporting lag. To provide faster detection of epidemics, recently approaches using unofficial data such as news reports, social media, and search queries are suggested. Collection of such data is cheap in cost and is realized in near real-time. This research aims to develop regression models for early detecting the outbreak of the seasonal influenza epidemics in Korea with keyword query information provided from the Naver (Korean representative portal site) trend services for PC and mobile device. We selected 20 key words likely to have strong correlations with influenza-like illness (ILI) based on literature review and proposed a logistic regression model and a multiple regression model to predict the outbreak of ILI. With respect of model fitness, the multiple regression model shows better results than logistic regression model. Also we find that a mobile-based regression model is better than PC-based regression model in estimating ILI percentages.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.709-714
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• 2009

Recently, biological researches are required to deal with a large scale of data. While scientists used classical experimental approaches for researches in the past, it is possible to get more sophisticated observations easily with convergence of information technologies and biology. The study on diseases is one of the most important issues of the life science. Conventional services and databases provide users with information such as classification of diseases, symptoms, and medical treatments through web. However, it is hard to connect or develop them for other new services because they have independent and different criterions. It may be a factor that interferes the development of biology. In this paper, we propose an integrated data structure for the disease database, and design and implement a novel directory system for diseases as an infrastructure for developing other new services.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.269-270
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• 2020

코로나 사태 이후에도 세상은 수많은 바이러스가 유행하게 될 것이다. 수많은 질병에서 필요한 것은 정보이고 이러한 정보를 얻기 위해서 사람들은 많은 사이트를 찾아다니며 정보를 검색하는 데 시간을 소비하고 원하는 정보를 빠르게 찾을 수 없다. 이러한 문제를 해결하고자 현재 유행하고 있는 질병 현황 정보, 시도별 현황 정보, 마스크 판매처 위치 및 재고, 바이러스 감염자 방문 기록을 확인할 수 있는 등 바이러스 정보를 짧은 시간에 사용자가 원하는 정보를 한 눈에 확인할 수 있도록 각종 사이트에서 데이터를 크롤링하여 가공하여 필요한 정보를 제공하는 반응형웹 시스템을 개발하였다.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.932-937
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• 2007

생의학 분야 문헌의 양이 빠르게 증가함에 따라, 생의학 연구자들이 필요로 하는 정보를 얻기가 어렵게 되었다. 이를 해결하기 위해, 인간-컴퓨터 상호작용 분야에서는 생의학 문헌 검색 시스템, 또는 생의학 문헌의 정보 추출 시스템 등에 대한 연구가 진행되고 있다. 본 논문에서는 생의학 문헌으로부터 정보를 자동으로 추출하기 위한 관계정보 추출 시스템에 대해 소개한다. 소개하는 시스템은 크게 요약 수집 모듈, 관계 추출 모듈, 관계 가시화 모듈로 구성되어 있다. 우선, 요약 수집 모듈에서는 특정 주제의 문헌들을 검색 및 수집한다. 그리고, 관계 추출 모듈에서는 수집된 문헌들에 대해서, 단백질/유전자 등의 생물학 개체를 인식하고, 구문분석을 통하여 인식된 개체들 사이의 관계를 추출한다. 마지막으로, 관계 가시화 모듈에서는 추출된 관계를 통합하여 네트워크 형태로 가시화한다. 이 시스템은 생물학 실험 이전의 문헌 기반 타당성 검사, 단백질-단백질 상호작용 또는 특정 질병과 유전자의 조절관계 분석, 또는 대용량 문헌 처리를 통한 패스웨이 데이터베이스 구축 등에 활용될 수 있다.

• The Microorganisms and Industry
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• v.15 no.2
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• pp.54-56
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• 1989

19세기 초기까지 인류가 사용해왔던 약물들은 동식물및 광물질 기원의 물질을 단순하게 처리한 것들이었다. 그 이후 화학의 발달및 필수적인 생리활성 검색법의 개발에 의해 비로소 현대적인 의미의 약물학(Pharmacology)의 시대가 열리게 되었다. 화학의 발달에 의해 오래전부터 사용되던 약제 중의 유효성분의 분리, 정제, 확인과 신물질의 합성이 가능해졌으며, 생리활성 검색법의 개발에 의해 유효성의 측정및 약물의 생체내 작용기전의 확인이 연구될 수 있었다. 일단 이러한 필수적인 도구와 테크닉이 주어지자 신약개발은 관련분야의 급속한 발달과 보조를 같이하여 가속적으로 발전하였으며 여기에 의약품 제조업의 성장이 자극제로 작용하였다. 그 결과 신개발약물의 양적인 팽창, 약물에 의해 조절될 수 있는 질병의 증가 및 약물작용기전의 이해등의 성과가 얻어질 수 있었다. 신개발약물의 수는 세계 제2차대전 종료후 10여년 사이에 최대에 달하였으나 그후 신약의 허가에 수반되는 법령의 강화에 따라 감소하기 시작하여 현재는 거의 일정한 수준을 유지하고 있다.

• Journal of the Korean Society of Radiology
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• v.16 no.4
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• pp.453-462
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• 2022

In this paper, the artificial intelligence (AI) technology used in the medical image analysis field was analyzed through a literature review. Literature searches were conducted on PubMed, ResearchGate, Google and Cochrane Review using the key word. Through literature search, 114 abstracts were searched, and 98 abstracts were reviewed, excluding 16 duplicates. In the reviewed literature, AI is applied in classification, localization, disease detection, disease segmentation, and fit degree of registration images. In machine learning (ML), prior feature extraction and inputting the extracted feature values into the neural network have disappeared. Instead, it appears that the neural network is changing to a deep learning (DL) method with multiple hidden layers. The reason is thought to be that feature extraction is processed in the DL process due to the increase in the amount of memory of the computer, the improvement of the calculation speed, and the construction of big data. In order to apply the analysis of medical images using AI to medical care, the role of physicians is important. Physicians must be able to interpret and analyze the predictions of AI algorithms. Additional medical education and professional development for existing physicians is needed to understand AI. Also, it seems that a revised curriculum for learners in medical school is needed.

• Journal of Life Science
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• v.28 no.11
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• pp.1379-1385
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• 2018

Glycans are attached to proteins as in glycoproteins and proteoglycans. They are found on the exterior surface of cells. O- and N-linked glycans are very common in eukaryotic cells but may also be found in prokaryotes. The interaction of cell surface glycans with complementary glycan binding proteins located on neighboring cells, other cell types, pathogens like virus, or bacteria is crucial in biologically and biomedically important processes like pathogen recognition, cell migration, cell-cell adhesion, development, and infection. Their implication in pathological condition, suggests an important role for glycans as disease markers. In addition, a great amount of research has been shown that appropriate glycosylation of a recombinant therapeutic protein is critical for product solubility, stability, pharmacokinetics and pharmacodynamics, bioactivity, and safety. Besides, cancer-associated glycosylation changes often involve sialic acid in glycan branch which play important roles in cell-cell interaction, recognition and immunological response. This review aims at giving a comprehensive overview of the glycan's biological function and describing the relevance among the glycosylation, disease diagnosis and treatment methods. Furthermore, the high-throughput analytic methods available to measure the profile changing patterns of glycan in the blood serum as well as possible underlying biochemical mechanisms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.1
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• pp.145-155
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• 2010

In this paper, we suggest a fish drug domain ontology to show an associated information and hierarchy together through concept-relation and inference mechanism instead of keyword matching. First, we investigate competency questions from workers of fishery industry and then we derive concepts and terminologies. Next, we present a process of fish drug ontology modelling using Protege-OWL editor, which is an extension of Protege that supports the Web Ontology Language(OWL). Last, we suggest the user interface of semantic web-based fish drug information system to search easily associated informations of fish drug using this ontology. It is to provide an effective search method that fish disease manager, fish farmer, and students majoring in fisheries can confirm details of diseases, fish, and drug evaluations associated with fish drug within one screen without moving to another position.