• 한국정보관리학회:학술대회논문집
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• 한국정보관리학회 2006년도 제13회 학술대회 논문집
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• pp.191-196
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• 2006

Traditionally, nurses keep the written patient records, which are referred as nursing care plan. Nursing care plan reports are one of the most important documents in the application of nursing processes. Typically, nurses prepare the plans by including general patient information as well as the patient's medical history information. In addition, the patient's developmental history and other specific health related information are part of the plans. The plans are usually concluded with the goals of the nursing care plan, nursing diagnoses, expected outcomes of the care, and possible nursing interventions. The nursing diagnoses, outcomes, and interventions are defined by North American Nursing Diagnosis Association (NANDA). This means that the nurses will select the appropriate diagnoses, outcomes, and interventions from an approved set. We developed a web-based nursing care plan generation system. In this paper, we report our work on developing a visual interface to the NANDA nursing diagnoses, outcomes, and interventions database as a part of the web-based nursing care plan generation system.

• 한국컴퓨터정보학회논문지
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• 제22권6호
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• pp.79-86
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• 2017

In this paper, we implement a common module that can integrate multiple biometric information for integrated patient monitoring system. Conventional biomedical instruments have many devices attached to each patient, making it difficult to monitor abnormality signs of many patients in real time. In this paper, we propose a module for an integrated monitoring system that can perform centralized monitoring using a common module that integrates multiple measurement devices. A protocol for sending and receiving packets between the measuring device and the common module is designed, and the packets transmitted through the network are stored and managed through the integrated monitoring system and provide information to various users such as medical staff. The results of this study are expected to contribute to the management of patients and efficient medical services in hospitals.

• 한국산학기술학회논문지
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• 제19권12호
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• pp.374-382
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• 2018

본 연구는 간호대학생을 대상으로 정보매체를 통한 환자안전사고 노출 정도와 학생들의 환자안전관련 지식, 인식, 수행자신감의 관계를 알아보기 위해 시행되었다. 연구의 대상자는 일개 여대의 간호학과에 재학 중인 대학생으로 서면동의서를 작성한 학생 348명 중 불충분한 설문지를 제외하고 337부를 자료 분석에 활용하였다. 자료수집은 2018년 6월 4일에서 12일까지 이루어졌으며 수집된 자료는 SPSS 21.0프로그램을 활용하여 기술 통계, t-test, one-way ANOVA, Pearson's correlation coefficient 방법으로 분석되었다. 연구결과 대상자의 환자안전에 대한 지식은 평균 6.43점, 인식은 평균 41.02점, 수행자신감은 평균 39.61점이었으며 TV와 인터넷 매체를 통해 환자안전사고에 노출된 정도는 각 1.25점과 1.35점으로 나타났다. 환자안전 지식은 연령, 학년, 환자안전 교육 경험에 따라 통계적으로 유의한 차이를 보였으며, 환자안전 인식은 전공만족도에 따라, 환자안전 수행자신감은 학년, 환자안전 교육 경험, 전공만족도에 따라 통계적으로 유의한 차이를 보였다. TV를 통한 환자안전사고 정보매체의 노출 정도는 지식(r=.32, p<.000), 수행자신감(r=.21, p<.000)과, 인터넷을 통한 정보매체의 노출 정도는 지식(r=.34, p<.000), 인식(r=.12, p=.028), 수행자신감(r=.24, p<.000)과 유의한 순상관관계를 보였다. 본 연구 결과를 바탕으로 임상실습 전부터 학생들에게 환자안전에 대한 체계적인 교육을 제공할 뿐 아니라 정보매체를 통해 전달되는 환자안전사고와 같은 이슈에 비판적 사고를 가지고 접근할 수 있는 환자안전 교육프로그램을 개발, 적용하고 그 효과를 평가하는 추후 연구를 제언하는 바이다.

• 디지털융복합연구
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• 제12권4호
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• pp.335-342
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• 2014

최근 노인인구의 증가하는 추세에 따라 만성질환자의 수도 증가하고 있으며 이에 따라 만성질환자들에 대한 건강 관리문제가 중요하게 대두되고 있다. 본 논문에서는 스마트 모바일 기기를 이용한 만성질환 관리 시스템을 구현하고자 한다. 제안한 만성질환 관리 시스템은 생체정보를 검출하는 생체계측센서와 그 센서로부터 정보를 수신하고 환자관리 서버로 정보를 전송하는 스마트 모바일 기기, 그리고 무선 통신망을 통해 전달받은 데이터를 해석하고 처리하기 위한 환자관리 서버, 환자관리 DB, 그리고 환자 증상분석 전문가 에이전트로 구성된다. 생체 신호는 심전도, 혈압, 혈당 및 PPG 등의 모듈로 구성하였다. 만성질환자에 대한 건강관리 시스템을 구현함으로서 측정되어진 생체 데이터를 모니터링하여 현재의 건강상태를 확인할 수 있었으며, 모바일 환경에서 환자군에게 맞는 개별적인 서비스를 제공할 수 있다는 점에서 의의가 있다.

• International Journal of Advanced Culture Technology
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• 제8권4호
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• pp.167-176
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• 2020

Recommendation Systems is the top requirements for many people and researchers for the need required by them with the proper suggestion with their personal indeed, sorting and suggesting doctor to the patient. Most of the rating prediction in recommendation systems are based on patient's feedback with their information regarding their treatment. Patient's preferences will be based on the historical behaviour of similar patients. The similarity between the patients is generally measured by the patient's feedback with the information about the doctor with the treatment methods with their success rate. This paper presents a new method of predicting Top Ranked Doctor's in recommendation systems. The proposed Recommendation system starts by identifying the similar doctor based on the patients' health requirements and cluster them using K-Means Efficient Clustering. Our proposed K-Means Clustering with Content Based Doctor Recommendation for Cancer (KMC-CBD) helps users to find an optimal solution. The core component of KMC-CBD Recommended system suggests patients with top recommended doctors similar to the other patients who already treated with that doctor and supports the choice of the doctor and the hospital for the patient requirements and their health condition. The recommendation System first computes K-Means Clustering is an unsupervised learning among Doctors according to their profile and list the Doctors according to their Medical profile. Then the Content based doctor recommendation System generates a Top rated list of doctors for the given patient profile by exploiting health data shared by the crowd internet community. Patients can find the most similar patients, so that they can analyze how they are treated for the similar diseases, and they can send and receive suggestions to solve their health issues. In order to the improve Recommendation system efficiency, the patient can express their health information by a natural-language sentence. The Recommendation system analyze and identifies the most relevant medical area for that specific case and uses this information for the recommendation task. Provided by users as well as the recommended system to suggest the right doctors for a specific health problem. Our proposed system is implemented in Python with necessary functions and dataset.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권7호
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• pp.2209-2224
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• 2022

The Internet of Things (IoT) is being used in a wide variety of fields due to the recent 4th industrial revolution. In particular, research is being conducted that combines IoT with the medical field such as telemedicine. Among them, the field of shock detection is a big issue in the medical field because the causes of shock are diverse, treatments are very complex, and require a high level of medical knowledge and experience. The transmission of infectious diseases is common when treating critically ill patients, especially patients with shock. Thus, to effectively care for shock patients, we propose an architecture that continuously monitors the patient's condition, and automatically recommends a drug injection treatment according to the patient's shock condition. The patient's hemodynamic information is continuously monitored, and the patient's shock generation information is recorded periodically. With the recorded patient information, the patient's condition is determined and automatically injected with necessary medication. The medical team can find out whether the patient's condition has improved by checking the recorded information through web applications. The study can help relieve the shortage of medical personnel and help prevent transmission of infectious disease in medical staff. We look forward to playing a role in helping medical staff by making recommendations for the diagnosis and treatment of complex and difficult shocks.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2003년도 춘계종합학술대회
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• pp.534-537
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• 2003

환자감시장치란 환자로부터 기본적인 생체신호들을 받아서 보여주는 장치이다. 그중 의사용 환자 감시장치는 환자의 건강상태를 알아야 하는 의사의 이동성을 보장하여, 언제든 원하는 곳으로 환자의 상태를 알리는 역할을 한다. 기존의 환자감시장치의 의사용 환자감시장치는 실시간 전송의 PPP 프로토콜 기반으로 네트워크를 구성하고 있다. 본 논문에서는 의사용 환자감시장치가 보다 자유롭고, 효율적이며, 충분한 기능을 할 수 있도록 설계하였다. CDMA의 SMS 프로토콜은 적은 양의 데이터를 보낼 때 효율적인 프로토콜로 적은 전력소모, 저렴한 이용요금, 적은 양의 전자파방출의 특징을 가지고 있다.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2002년도 춘계종합학술대회
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• pp.366-372
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• 2002

Tele-medicine and emergency medical system are necessary for moving from an accidental point or far distance to a hospital and emergency treatment or home treatment before a hospital. Emergency treatment is extremely important in the case of death before arriving a hospital and deformed or disabled by medical treatment delay. A necessary element for this medical system is the emergency communication system. This system is on preparing for an ability of furnishing patient status to a corresponding health service by monitoring the patient at an ambulance of the accident place. This is the transportation of basic biological information of a patient to a medical center by wireless communication system and the corresponding hospital or medical center examine the patient by monitoring, then they can send emergency medical order to the patient for emergency treatment. The TRS is most efficient way of emergency medical communication system, which is currently used with popularity. In this paper studied simultaneously a way of detecting and transporting bio-logical signals, and monitoring of transporting data with communication of voice in the accident place or ambulance.

• 한국정보통신학회논문지
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• 제14권3호
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• pp.595-602
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• 2010

EMR(Electronic Medical Record)이란 종이문서 방식을 사용하는 의료차트 대신에 모니터로 출력되는 형식의 의료차트를 말한다. 현재 통용되는 EMR은 전문 의료인이 전문적인 용어 써놓은 양식이기 때문에, 그 의료기록의 실제 주인인 환자는 의료인의 부가적인 설명 없이는 자신의 자세한 증세는 물론 병명조차 정확히 확인하기 힘들다. 이에 환자에게 있어 매우 시간 소모적이고 노동집약적인 과정으로 환자가 의료인을 거쳐서 자신의 의료정보를 얻는 방식보다는 환자가 직접 그 정보를 얻는 방식이 필요하다. 따라서 본 연구는 EMR이 갖는 이러한 문제를 해결하기 위해 환자가 의료인을 거치지 않고 직접 자신의 의료정보를 얻기 위한 환자 접근행 EMR 응용 시스템의 비즈니스 모델을 제시한다.

• Genomics & Informatics
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• 제18권2호
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• pp.17.1-17.4
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• 2020

The amount of content on social media platforms such as Twitter is expanding rapidly. Simultaneously, the lack of patient information seriously hinders the diagnosis and treatment of rare/intractable diseases. However, these patient communities are especially active on social media. Data from social media could serve as a source of patient-centric knowledge for these diseases complementary to the information collected in clinical settings and patient registries, and may also have potential for research use. To explore this question, we attempted to extract patient-centric knowledge from social media as a task for the 3-day Biomedical Linked Annotation Hackathon 6 (BLAH6). We selected amyotrophic lateral sclerosis and multiple sclerosis as use cases of rare and intractable diseases, respectively, and we extracted patient histories related to these health conditions from Twitter. Four diagnosed patients for each disease were selected. From the user timelines of these eight patients, we extracted tweets that might be related to health conditions. Based on our experiment, we show that our approach has considerable potential, although we identified problems that should be addressed in future attempts to mine information about rare/intractable diseases from Twitter.