• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2007년도 춘계학술발표대회
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• pp.617-620
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• 2007

최근 건강에 대한 관심이 높아지면서 의료 분야를 지원하는 애플리케이션 개발이 활발히 이루어지고 있다. 심장의 상태를 곡선으로 나타내는 ECG 를 기반으로 심장병의 유무를 발견하는 애플리케이션은 의료 분야 애플리케이션의 좋은 예라고 할 수 있다. ECG 만으로 질환을 판단하는 것은 제약이 있어, 이를 극복하기 위해 MCG 혹은 가상심장과 같은 다른 자원을 활용하는 것은 좋은 방법이다. 이와 같은 통합 시스템을 지원하려면 각 도메인에 대한 지식이 정의되어야 한다. 이에 본 연구에서는 ECG 와 심장계 질환에 대한 지식을 온톨로지를 이용하여 구축하고 ECG 를 통해 질환을 발견할 수 있는 추론 시스템을 제안하고 프로토타입 시스템을 개발한다.

• 한국IT서비스학회지
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• 제18권2호
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• pp.123-141
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• 2019

The goal of this study is to review the major research trend on the convergence studies of AI and healthcare technologies. For the study, 15,260 English articles on AI and healthcare related topics were collected from Scopus for 55 years from 1963, and text mining techniques were conducted. As a result, seven key research topics were defined : "AI for Clinical Decision Support System (CDSS)", "AI for Medical Image", "Internet of Healthcare Things (IoHT)", "Big Data Analytics in Healthcare", "Medical Robotics", "Blockchain in Healthcare", and "Evidence Based Medicine (EBM)". The result of this study can be utilized to set up and develop the appropriate healthcare R&D strategies for the researchers and government. In this study, text mining techniques such as Text Analysis, Frequency Analysis, Topic Modeling on LDA (Latent Dirichlet Allocation), Word Cloud, and Ego Network Analysis were conducted.

• 전자공학회논문지CI
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• 제46권6호
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• pp.7-17
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• 2009

효과적인 재활 시스템을 구상하는데 있어서 훈련 데이터의 정교한 분석은 다음 단계 훈련을 위한 피드백 자료로서 매우 중요하다. 현재 다양한 생체 역학적 실험을 통해 인간의 운동 능력을 평가하고 이로부터 생성된 데이터의 분석을 위한 객관적이고 신뢰성 있는 연구결과들이 발표되고 있다. 그러나 대부분의 기존 연구들은 기초 통계적인 방법에 근거한 정량분석만을 수행함으로써, 획득된 정보를 임상에 적용 하는데 있어서는 충분한 신뢰성을 보장할 수 없다. 데이터마이닝은 대용량 데이터에 들어있는 숨겨진 규칙과 패턴을 탐사함으로써 임상 데이터에 숨어있는 의미 있는 정보추출에 성공적으로 사용되고 있으며, 특히 임상 연구 분야에서는 훌륭한 의사 결정 지원 시스템으로서 점점 그 사용이 증가되고 있다. 본 연구에서는 신뢰성 있는 자세 제어 능력(Postural control ability) 평가를 위해서 측정된 훈련 데이터에 가중연관규칙 탐사를 적용하여 자세 훈련 유형에 따른 근육 활성 패턴과의 연관성을 분석, 효율적인 재활 훈련 규칙을 탐사하였다. 탐사된 규칙은 재활 및 임상 전문가의 의사결정에 더욱 정성적이고 유용한 선험적 지식으로 사용 될 수 있으며, 이를 근거로 환자 맞춤형 최적의 재활 훈련 모델을 구상하기 위한 지표로서 사용될 수 있다.

• Journal of Hospice and Palliative Care
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• 제24권2호
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• pp.130-134
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• 2021

In the era of coronavirus disease 2019 (COVID-19), social distancing and strict visitation policies at hospitals have made it difficult for medical staff to provide high-quality end-of-life (EOL) care to dying patients and their families. There are various issues related to EOL care, including psychological problems of patients and their families, difficulties in EOL decision-making, the complicated grief of the bereaved family, moral distress, and exhaustion of medical staff. In relation to these issues, we aimed to discuss practical considerations in providing high-quality EOL care in the COVID-19 pandemic. First, medical staff should discuss advance care planning as early as possible and use the parallel planning strategy. Second, medical staff should play a role in facilitating patient-family communication. Third, medical staff should actively and proactively evaluate and alleviate dying patients' symptoms using non-verbal communication. Lastly, medical staff should provide care for family members of the dying patient, who may be particularly vulnerable to post-bereavement problems in the COVID-19 era. Establishing a system of screening high-risk individuals for complicated grief and connecting them to bereavement support services might be considered. Despite the challenging and limited environment, providing EOL care is essential for patients to die with dignity in peace and for the remaining family to return to life after the loved one's death. Efforts considering the practical issues faced by all medical staff and healthcare institutions caring for dying patients should be made.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2006년도 가을 학술발표논문집 Vol.33 No.2 (A)
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• pp.28-32
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• 2006

최근 연구결과에 의하면 심장질환을 포함한 심혈관질환은 성별에 관계없이 미국 및 전 세계적으로 질병사망의 주요 원인으로 조사되었다. 본 연구에서는 보다 효율적으로 진단하기 위해 진단의사 결정 보조시스템에 대해서 다룬다. 개발된 시스템은 혈청 내의 특정 단백질의 상대적 양을 측정할 수 있는 바이오칩인 압타머칩을 이용해 생성한 환자들의 칩 데이터를 Support Vector Machine, Neural Network, Decision Tree, Bayesian Network 등의 총 4가지 기계학습 알고리즘으로 분석하여 질환단계를 예측하고 진단을 위한 보조정보를 제공한다. 논문에서는 총 135개 샘플로 구성된 3K 압타머칩 데이터에 대해 측정된 초기 시스템의 질환단계 분류성능을 제시하고 보다 유용한 진단의사결정 보조 시스템을 구성하기 위한 요소들에 대해서 논의한다.

• 여성건강간호학회지
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• 제12권2호
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• pp.87-96
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• 2006

Purpose: This study was conducted to provide fundamental information for a system establishment of advanced practice nursing for gynecological cancer patients (APN-GCP). Method: Data was collected by focus group and individual interviews and analyzed in the framework of the Grounded theory method mapped by Strauss and Corbin (1990). There were 13 subjects in this study (nurses, doctors, patient and her family). Result: We identified 87 concepts, 22 sub-categories, and 10 categories. Categories for role expectation were arrangement of diagnosis and treatment process, giving information of treatment course, support of treatment process, patients' right toward making a decision of treatment, counseling and teaching after discharge from hospital, medical insurance and financial problems, counseling about sexual problems and use of family and community resources. All subjects perceived the necessity of an APN-GCP. An APN-GCP requires over 2$\sim$7 years clinical experience and a master's degree. Services would be performed from initial registration to termination of treatment or death, and accomplished on an outpatient clinic basis. Conclusion: The nursing delivery system and curriculum should be developed for a women's health nurse practitioner including APN-GCP. As a further step, cost-effectiveness and projected estimation of manpower of APN-GCP should be studied in the future.

• International Journal of Computer Science & Network Security
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• 제23권12호
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• pp.225-234
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• 2023

In this era of information and communication technology (ICT), tremendous improvements have been witnessed in our daily lives. The impact of these technologies is subjective and negative or positive. For instance, ICT has brought a lot of ease and versatility in our lifestyles, on the other hand, its excessive use brings around issues related to physical and mental health etc. In this study, we are bridging these both aspects by proposing the idea of AI based mental healthcare (AIMS). In this regard, we aim to provide a platform where the patient can register to the system and take consultancy by providing their assessment by means of a chatbot. The chatbot will send the gathered information to the machine learning block. The machine learning model is already trained and predicts whether the patient needs a treatment by classifying him/her based on the assessment. This information is provided to the mental health practitioner (doctor, psychologist, psychiatrist, or therapist) as clinical decision support. Eventually, the practitioner will provide his/her suggestions to the patient via the proposed system. Additionally, the proposed system prioritizes care, support, privacy, and patient autonomy, all while using a friendly chatbot interface. By using technology like natural language processing and machine learning, the system can predict a patient's condition and recommend the right professional for further help, including in-person appointments if necessary. This not only raises awareness about mental health but also makes it easier for patients to start therapy.

• 간호행정학회지
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• 제8권3호
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• pp.411-430
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• 2002

Purpose : This study was to develop Nursing Process Model of abdominal surgery patient using nursing diagnoses of NANDA, Nursing Interventions Classification(NIC), and Nursing Outcomes Classification(NOC). Method : The data in database were collected from nursing records in sixty patients with abdominal surgery admitted in a university hospital and open questionnaires of thirteen nurses. Systematic nursing process resulting from each nursing diagnoses, most common, was developed by the statistical analysis through database query from clinical database of abdominal surgery patients. Result : 51 nursing diagnoses were identified in abdominal surgery patients. The most commonly occurred nursing diagnoses were Pain, Risk for Infection, Sleep Pattern Disturbance, Hyperthermia, Altered Nutrition: Less Than Body Requirements in order. The linkage lists of NANDA to NIC and NANDA to NOC, and the nursing activities according to nursing diagnoses of abdominal surgery patients were identified in unit. Conclusion : Nursing Process of abdominal surgery patients was comprised of core nursing diagnoses, core nursing interventions, core nursing outcomes which provides the most reliable data in unit and could make nurses facilitate nursing process easily without full consideration of knowledge about nursing language classification system. Therefore, it could support nurses' decision making and recording of nursing process especially in the computerized patient record system if unit nursing process model using standardized nursing language system which contains of their own core nursing process data was developed.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제21권7호
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• pp.464-469
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• 2015

모바일 기기를 활용한 홀터 모니터링으로 환자의 개인별 심전도 신호의 장주기 수집이 가능해졌다. 하지만 이에 따른 의사 결정 지원 도구 및 응용에 대한 연구는 미흡한 실정이다. 본 논문에서는 장주기로 수집된 심전도 신호의 대표패턴을 추출하기 위한 축약 알고리즘을 제안한다. 그리고 추출된 대표패턴을 이용하여 유사한 환자의 목록을 제공하는 검색기를 소개한다. 사례분석을 통해 제안한 유사환자 검색기가 대표패턴을 통해 전문가의 임상활동을 간소화 하며, 유사한 환자의 목록을 제공하여 축적 데이터의 높은 활용 가능성을 제고함을 보였다. 또한, MIT-BIH 부정맥 데이터베이스를 이용한 평가에서, 축약 알고리즘이 64%의 레코드에 대해 단순화된 대표패턴을 제공하며, 부정맥 분류 결과를 평균 98% 축소함을 보였다.

• 한국의료질향상학회지
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• 제22권2호
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• pp.43-73
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• 2016

Purpose: The aims of this study were to assess the presence of core patient safety practices in Korean hospitals and assess the differences in reporting and learning systems of patient safety, infrastructure, and safe practices by hospital characteristics. Methods: The authors developed a questionnaire including 39 items of patient safety staffing, health information system, reporting system, and event-specific prevention practices. The survey was conducted online or e-mail with 407 tertiary, general and specialty hospitals. Results: About 90% of hospitals answered the self-reporting system of patient safety related events is established. More than 90% of hospitals applied incidence monitoring or root cause analysis on healthcare-associated infection, in-facility pressure ulcers and falls, but only 60% did on surgery/procedure related events. More than 50% of the hospitals did not adopted present on admission (POA) indicators. One hundred (80.0%) hospitals had a department of patient safety and/or quality and only 52.8% of hospitals had a patient safety officer (PSO). While 82.4% of hospitals used electronic medical records (EMRs), only 53% of these hospitals adopted clinical decision support function. Infrastructure for patient safety except EMRs was well established in training, high-level and large hospitals. Most hospitals implemented prevention practices of adverse drug events, in-facility pressure ulcers and falls (94.4-100.0%). But prevention practices of surgery/procedure related events had relatively low adoption rate (59.2-92.8%). Majority of prevention practices for patient safety events were also implemented with a relatively modest increase in resources allocated. Conclusion: The hospital-based reporting and learning system, EMRs, and core evidence-based prevention practices were implemented well in high-level and large hospitals. But POA indicator and PSO were not adopted in more than half of surveyed hospitals and implementation of prevention practices for specific event had low. To support and monitor progress in hospital's patient safety effort, national-level safety practices set is needed.