• 한국콘텐츠학회논문지
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• 제21권12호
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• pp.13-22
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• 2021

임상의사결정시스템은 누적된 의료 데이터를 활용하여 머신러닝으로 학습된 AI 모델을 환자의 진단 및 진료 예측에 적용한다. 그러나 기존의 블랙박스 기반의 AI 응용은 시스템이 예측한 결과에 대해 타당한 이유를 제시하지 못하여 설명성이 부족한 한계점이 존재한다. 이와 같은 문제점을 보완하기 위해 이 논문에서는 임상의사결정시스템의 개발 단계에서 설명이 가능한 XAI를 적용하는 시스템 모델을 제안한다. 제안 모델은 기존의 AI모델에 설명성이 가능한 특정 XAI 기술을 추가로 적용시켜 블랙박스의 한계점을 보완할 수 있다. 제안 모델의 적용을 보이기 위해 LIME과 SHAP을 활용한 XAI 적용 사례를 제시한다. 테스트를 통해 데이터들이 모델의 예측 결과에 어떤 영향을 미치는지 다양한 관점에서 설명할 수 있다. 제안된 모델은 사용자에게 구체적인 이유를 제시함으로써 사용자의 신뢰를 높일 수 있는 장점을 가진다. 아울러 XAI의 적극적인 활용을 통해 기존 임상의사결정시스템의 한계를 극복하고 더 나은 진단 및 의사결정 지원을 가능하게 할 것으로 기대한다.

• 대한간호학회지
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• 제36권4호
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• pp.652-661
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• 2006

Purpose. The purposes of this study were to apply data mining tool to nursing specific knowledge discovery process and to identify the utilization of data mining skill for clinical decision making. Methods. Data mining based on rough set model was conducted on a large clinical data set containing NMDS elements. Randomized 1000 patient data were selected from year 1998 database which had at least one of the five most frequently used nursing diagnoses. Patient characteristics and care service characteristics including nursing diagnoses, interventions and outcomes were analyzed to derive the meaningful decision rules. Results. Number of comorbidity, marital status, nursing diagnosis related to risk for infection and nursing intervention related to infection protection, and discharge status were the predictors that could determine the length of stay. Four variables (age, impaired skin integrity, pain, and discharge status) were identified as valuable predictors for nursing outcome, relived pain. Five variables (age, pain, potential for infection, marital status, and primary disease) were identified as important predictors for mortality. Conclusions. This study demonstrated the utilization of data mining method through a large data set with stan dardized language format to identify the contribution of nursing care to patient's health.

• 한국융합학회논문지
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• 제13권3호
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• pp.67-75
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• 2022

의료정보는 의료기기뿐만 아니라 카메라 등의 기기로부터 다양하게 생성된다. 최근 의료빅데이터 수집 및 관리에서부터 환자의 상태분석을 위한 의료인공지능 제품 및 관련 융합기술들이 급격히 증가하고 있지만, 실용화까지의 절차들이 산재되어 있어 실적용에 어려움을 겪고 있다. 본 논문에서는 의료인공지능 기술 연구, 개발 및 실용화 절차를 간소화하고, 관련 산업 발전 가속화를 위한 지능형 병원정보시스템 모델을 제안한다. 제안한 모델은 의료기관에서 (1)다양한 기기로부터 환자 데이터의 실시간 관리, (2)의료인공지능 기술 개발에 특화된 데이터 정제 및 관리, (3)개발된 의료인공지능 기술의 실시간 적용을 통합 지원한다. 이를 이용하여 환자모니터링기기로부터 실시간 생체데이터 수집 및 의료인공지능 특화 데이터 생성 사례와 기 개발된 카메라 기반 환자 보행분석 및 뇌MRA 기반 뇌혈관질환분석 기술의 구체적 적용사례를 소개한다. 제안한 모델을 기반으로 인공지능 개발에 필요한 데이터의 보안성 증대 및 일관된 인터페이스의 플랫폼화를 통한 실용화 증대로 병원정보시스템 개선에 활용되기를 기대한다.

• 보건행정학회지
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• 제8권1호
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• pp.183-202
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• 1998

This study is to investigate patient's choice of health care and the demand for Korean traditional medicine care in rural areas in 1995. It tried to evaluate the effect of out-of-pocket expenditure, travel time, and waiting time on improving care-seeking and substituting clinical medicine for pharmacy care and Korean traditional medicine care in rural areas. The statistical model of this study is conditional logit to estimate effects of choice-specific and individual-specific characteristics on the choice of type of services. This study used, as explanatory variables, average out-of-pocket payment, travel time, and waiting time of services required to use the services. The model was empirically tested using data from 1995 Korean National Health Survery. The results showed that rural Koreans responded to out-of pocket payment and travel time. Increases of out-of-pocket payment and travel time decreased the probability to choose care in rural Korea. Rural Koreans were more likely to seek care than others with low out-of-pocket payment and travel time. The probability of choosing Korean traditional medicine were higher among the members of the households with higher education level and older persons, while they were lower in the households with large family than others compared with the probabilities of choosing public health facilities. The result of this study implies that policy on use of health care in rural Korea can be focused in managing travel time and out-of-pocket payment.

• 대한방사선기술학회지:방사선기술과학
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• 제43권6호
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• pp.503-509
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• 2020

This study aims to develop a patient-specific radiation exposure dose prediction model based on anthropometric data that can be easily measurable during CT examination, and to be used as basic data for DRL setting and radiation dose management system in the future. In addition, among the machine learning algorithms, the most suitable model for predicting exposure doses is presented. The data used in this study were chest CT scan data, and a data set was constructed based on the data including the patient's anthropometric data. In the pre-processing and sample selection of the data, out of the total number of samples of 250 samples, only chest CT scans were performed without using a contrast agent, and 110 samples including height and weight variables were extracted. Of the 110 samples extracted, 66% was used as a training set, and the remaining 44% were used as a test set for verification. The exposure dose was predicted through random forest, linear regression analysis, and SVM algorithm using Orange version 3.26.0, an open software as a machine learning algorithm. Results Algorithm model prediction accuracy was R^2 0.840 for random forest, R^2 0.969 for linear regression analysis, and R^2 0.189 for SVM. As a result of verifying the prediction rate of the algorithm model, the random forest is the highest with R^2 0.986 of the random forest, R^2 0.973 of the linear regression analysis, and R^2 of 0.204 of the SVM, indicating that the model has the best predictive power.

• 대한기계학회논문집A
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• 제28권11호
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• pp.1664-1671
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• 2004

It is necessary to have a model that describes the feature of the knee Joint with a sufficient accuracy. Koreans, however, do not have their own knee joint model to be used in the total knee replacement arthroplasty. They have to use European or American models which do not match Koreans. Three-dimensional visualization techniques are found to be useful in a wide range of medical applications. Three-dimensional imaging studies such as CT(computed tomography) and MRI(magnetic resonance image) provide the primary source of patient-specific data. Three-dimensional knee joint models were constructed by image processing of the CT data of 10 subjects. Using the constructed model, the dimensions of Korean knee joint were measured. And this study proposed a three-dimensional model and data, which can be helpful to develop Korean knee implants and to analyze knee joint movements.

• 품질경영학회지
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• 제51권3호
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• pp.381-401
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• 2023

Purpose: The purpose of this study was to understand the perception of medical staff to propose an infected patient transport robot as a means of responding to infectious diseases. Methods: The data collected through the survey was analyzed through AHP analysis. The measurement tools used in this study were derived through the SERVQUAL model and Focus Group Interview(FGI), and consisted of four detailed questions for each of five classes: tangible, reliability, responsiveness, assurance, and empathy. Results: As a result of the study, there are concerns about risk factors that may occur in areas where medical staff intervention is minimized. Above all, we confirmed the consensus that safety should be the top priority during the process of robots to transport patients. In particular, highlighted were the resolution of device errors that may occur during the process for transporting patients and easy provision of the first aid. Additionally, the ability to monitor patients and suppress infection factors turned out to be important, which was directly related to the simplification of the role of medical staff and work efficiency. Conclusion: As one of the means of effectively controlling infectious diseases in a pandemic situation, a robot to transport the infected patient was considered. However, in order to commercialize this, specific verification of the safety of medical staff and patients is needed, and empirical data on providing the first aid, patient monitoring, and infection factor suppression should be presented.

• Journal of Veterinary Science
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• 제22권1호
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• pp.7.1-7.13
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• 2021

Background: Niemann-Pick disease type C (NPC) is caused by the mutation of NPC genes, which leads to the abnormal accumulation of unesterified cholesterol and glycolipids in lysosomes. This autosomal recessive disease is characterized by liver dysfunction, hepatosplenomegaly, and progressive neurodegeneration. Recently, the application of induced neural stem cells (iNSCs), converted from fibroblasts using specific transcription factors, to repair degenerated lesions has been considered a novel therapy. Objectives: The therapeutic effects on NPC by human iNSCs generated by our research group have not yet been studied in vivo; in this study, we investigate those effects. Methods: We used an NPC mouse model to efficiently evaluate the therapeutic effect of iNSCs, because neurodegeneration progress is rapid in NPC. In addition, application of human iNSCs from NPC patient-derived fibroblasts in an NPC model in vivo can give insight into the clinical usefulness of iNSC treatment. The iNSCs, generated from NPC patientderived fibroblasts using the SOX2 and HMGA2 reprogramming factors, were transplanted by intracerebral injection into NPC mice. Results: Transplantation of iNSCs showed positive results in survival and body weight change in vivo. Additionally, iNSC-treated mice showed improved learning and memory in behavior test results. Furthermore, through magnetic resonance imaging and histopathological assessments, we observed delayed neurodegeneration in NPC mouse brains. Conclusions: iNSCs converted from patient-derived fibroblasts can become another choice of treatment for neurodegenerative diseases such as NPC.

• The Journal of Korean Physical Therapy
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• 제15권1호
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• pp.155-170
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• 2003

Lumbar segmental instability is considered to represent a significant sub-group within the chronic low back pain population. This condition has a unique clinical presentation that displays its symptoms and movement dysfunction within the neutral zone of the motion segment. The loosening of the motion segment secondary to injury and associated dysfunction of the local muscle system renders it biomechanically vulnerable in the neutral zone. There in evidence of muscle dysfunction related to the control of the movement system. There is a clear link between reduced proprioceptive input, altered slow motor unit recruitment and the development of chronic pain states. Dysfunction in the global and local muscle systems in presented to support the development of a system of classification of muscle function and development of dysfunction related to musculoskeletal pain. The global muscles control range of movement and alignment, and evidence of dysfunction is presented in terms of imbalance in recruitment and length between the global stability muscles and the global mobility muscles. The local stability muscles demonstrate evidence of failure of aeequate segmental control in terms of allowing excessive uncontrolled translation or specific loss of cross-sectional area at the site of pathology Motor recruitment deficits present as altered timing and patterns of recruitment. The evidence of local and global dysfunction allows the development of an integrated model of movement dysfunction. The clinical diagnosis of this chronic low back pain condition is based on the report of pain and the observation of movement dysfunction within the neutral zone and the associated finding of excessive intervertebral motion at the symptomatic level. Four different clinical patterns are described based on the directional nature of the injury and the manifestation of the patient's symptoms and motor dysfunction. A specific stabilizing exercise intervention based on a motor learning model in proposed and evidence for the efficacy of the approach provided.

• 한국IT서비스학회지
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• 제22권5호
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• pp.87-98
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• 2023

Traditionally, tooth restoration has been carried out by replicating teeth using plaster-based materials. However, recent technological advances have simplified the production process through the introduction of computer-aided design(CAD) systems. Nevertheless, dental restoration varies among individuals, and the skill level of dental technicians significantly influences the accuracy of the manufacturing process. To address this challenge, this paper proposes an approach to designing personalized tooth restorations using Generative Adversarial Network(GAN), a widely adopted technique in computer vision. The primary objective of this model is to create customized dental prosthesis for each patient by utilizing 3D data of the specific teeth to be treated and their corresponding opposite tooth. To achieve this, the 3D dental data is converted into a depth map format and used as input data for the GAN model. The proposed model leverages the network architecture of Pixel2Style2Pixel, which has demonstrated superior performance compared to existing models for image conversion and dental prosthesis generation. Furthermore, this approach holds promising potential for future advancements in dental and implant production.