• 전자공학회논문지CI
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• 제48권2호
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• pp.28-37
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• 2011

최근 건강에 대한 관심이 고조 되면서 발과 다리에 대한 진단, 치료, 예방의 전반적인 진료를 맡고 있는 족부의학(Podiatry)이 주목받고 있지만 국내 연구는 미비한 실정이다. 또한 임상 데이터 분석에 있어 대부분의 기존 연구들은 기초 통계적인 방법에 근거한 정량분석만을 수행함으로서, 획득된 정보를 임상에 적용 하는데 있어서는 충분한 신뢰성을 보장할 수 없다. 임상데이터 마이닝은 데이터마이닝의 다양한 분석 방법론을 이용하여 의료 현장에서 발생한 임상 데이터를 분석함으로서 전문가의 진단과 치료 과정의 결정에 도움을 주고 있다. 결정트리(Decision Tree) 알고리즘은 분석과정의 설명과 표현성이 좋고, 결과에 대한 해석이 편리하여 임상에서 적용하기가 용이하다. 본 연구에서는 신뢰성 있는 족부 임상 진단 평가를 위해 충남대학교병원 재활의학과 신발클리닉에 내원한 환자 1310명(남자:633명, 여자:677명)의 2620족(foot)을 대상으로 수집된 진료 데이터에 결정트리를 적용하여 22개의 족부 질환 인자에 따르는 15개의 족부 질환을 분류하고 그에 대한 64개의 진단 규칙을 탐사 하였다. 또한 5개의 클래스(영유아, 소아, 청소년, 노인, 전체)로 분류된 각 그룹들로부터 생성된 결정 트리를 통해 각 클래스의 질환 특징과 질환 주요 인자, 클래스 간 상관관계를 비교, 분석하였다. 탐사된 결과는 족부 임상 전문가의 의사결정에 더욱 정성적이고 유용한 선험적 지식을 제공할 것이고, 효과적이고 정확한 진단과 예측을 위한 임상 도구로써 사용될 수 있다.

• Asia pacific journal of information systems
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• 제16권2호
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• pp.69-84
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• 2006

Importance of Today's diffusion of integrated hospital information systems is that various and huge amount of data is being accumulated in their database systems. Many researchers have studied utilizing such hospital data. While most researches were conducted mainly for medical diagnosis, there have been insufficient studies to develop medical care cost prediction model, especially using machine learning techniques. In this research, therefore, we built a medical care cost prediction model for cancer patients using CBR (Case-Based Reasoning), one of the machine learning techniques. Its performance was compared with those of Neural Networks and Decision Tree models. As a result of the experiment, the CBR prediction model was shown to be the best in general with respect to error rate and linearity between real values and predicted values. It is believed that the medical care cost prediction model can be utilized for the effective management of limited resources in hospitals.

• 대한영상의학회지
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• 제83권3호
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• pp.737-743
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• 2022

아스페르길루스 기관-기관지염은 침습성 폐 아스페르길루스의 매우 드문 형태 중 하나로 주로 기관-기관지에 국한되어 거짓막이나 궤양을 형성하거나 폐쇄를 유발하는 질환이다. 거짓막성 아스페르길루스 기관-기관지염은 아스페르길루스 기관-기관지염 중 가장 심한 형태로 대게는 면역저하자에서 발병하고 예후가 좋지 않다. 현재까지 이 질환에 대해 몇 개의 국내보고가 있으나 영상 소견에 대한 보고는 드물다. 이에 저자들은 기관지경 검사상 거짓막성 아스페르길루스 기관-기관지염으로 진단되고 적절한 항진균제 투여로 성공적으로 치료된 환자의 증례를 특징적인 영상 소견과 함께 보고하고자 한다.

• Journal of Korean Neurosurgical Society
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• 제67권1호
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• pp.94-102
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• 2024

Objective : The spontaneous intracerebral hemorrhage (ICH) remains a significant cause of mortality and morbidity throughout the world. The purpose of this retrospective study is to develop multiple models for predicting ICH outcomes using machine learning (ML). Methods : Between January 2014 and October 2021, we included ICH patients identified by computed tomography or magnetic resonance imaging and treated with surgery. At the 6-month check-up, outcomes were assessed using the modified Rankin Scale. In this study, four ML models, including Support Vector Machine (SVM), Decision Tree C5.0, Artificial Neural Network, Logistic Regression were used to build ICH prediction models. In order to evaluate the reliability and the ML models, we calculated the area under the receiver operating characteristic curve (AUC), specificity, sensitivity, accuracy, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR). Results : We identified 71 patients who had favorable outcomes and 156 who had unfavorable outcomes. The results showed that the SVM model achieved the best comprehensive prediction efficiency. For the SVM model, the AUC, accuracy, specificity, sensitivity, PLR, NLR, and DOR were 0.91, 0.92, 0.92, 0.93, 11.63, 0.076, and 153.03, respectively. For the SVM model, we found the importance value of time to operating room (TOR) was higher significantly than other variables. Conclusion : The analysis of clinical reliability showed that the SVM model achieved the best comprehensive prediction efficiency and the importance value of TOR was higher significantly than other variables.

• 디지털융복합연구
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• 제18권6호
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• pp.401-409
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• 2020

본 연구는 환자중심성 의료문화 조성을 위한 의료기관 경영전략에 도움이 될 수 있는 융복합 입원 의료서비스 환자경험 관리모형을 개발하고자 하였다. '2018 의료서비스경험조사' 원시자료를 이용하여 만 15세 이상 입원 의료서비스 경험이 있는 593명을 분석하였다. 의사결정나무 모형을 활용하여 입원 의료서비스 경험에 대한 전반적 만족도와 환자 경험 추천 의사 예측모형을 개발하였으며 유형은 4개, 7개로 각각 분류되었다. 모형의 정확도는 68.9%, 78.3%였다. 입원 의료서비스 환자경험 전반적 만족도 결정요인은 간호사 영역과 병실 소음관리 영역이었으며 추천 의사 결정요인은 간호사 영역이었다. 입원 의료서비스 환자경험 관리모형을 제시하고 간호사 영역과 병실 소음관리 영역이 입원환자 경험에 중요한 요인임을 확인한 점이 의의가 있다. 입원 의료서비스 환자경험 관리모형의 일반화를 위한 추가 연구가 필요하다 생각된다.

• Journal of Microbiology and Biotechnology
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• 제30권6호
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• pp.885-892
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• 2020

Chemotherapy regimens for non-small cell lung cancer (NSCLC) have various adverse effects on the human body. For this reason, probiotics have received attention regarding their potential value as a safe and natural complementary strategy for cancer prevention. This study analyzed the anticancer effects of aqueous extracts of probiotic bacteria Bifidobacterium bifidum (BB), Bifidobacterium longum (BL), Bifidobacterium lactis (BLA), Bifidobacterium infantis 1 (BI1), and Bifidobacterium infantis 2 (BI2) on NSCLC cell lines. When the aqueous extracts of probiotic Bifidobacterium species were applied to the NSCLC cell lines A549, H1299, and HCC827, cell death increased considerably; in particular, the aqueous extracts from BB and BLA markedly reduced cell proliferation. p38 phosphorylation induced by BB aqueous extract increased the expression of cleaved caspase 3 and cleaved poly (ADP-ribose) polymerase (PARP), consequently inducing the apoptosis of A549 and H1299 cells. When the p38 inhibitor SB203580 was applied, phosphorylation of p38 decreased, and the expression of cleaved caspase 3 and cleaved PARP was also inhibited, resulting in a reduction of cell death. In addition, BB aqueous extracts reduced the secretion of MMP-9, leading to inhibition of cancer cell invasion. By contrast, after transfection of short hairpin RNA shMMP-9 (for a knockdown of MMP-9) into cancer cells, BB aqueous extracts treatment failed to suppress the cancer cell invasiveness. According to our results about their anticancer effects on NSCLC, probiotics consisting of Bifidobacterium species may be useful as adjunctive anticancer treatment in the future.

• 종양간호연구
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• 제5권1호
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• pp.3-10
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• 2005

Back ground: Nurses now occupy one third of all hospital human resources. Therefore, efficient management of nursing manpower is getting more important. While it is very clear that nursing workload requirement analysis and patient severity classification should be done first for the efficient allocation of nursing workforce, these processes have been conducted manually with ad hoc rule. Purposes: This study was tried to make a predict model for patient classification according to nursing need. We tried to find the easier and faster method to classify nursing patients that can help efficient management of nursing manpower. Methods: The nursing patient classifications data of the hospitalized cancer patients in one of the biggest cancer center in Korea during 2003.1.1-2003.12.31 were assessed by trained nurses. This study developed a prediction model and analyzing nursing needs by data mining techniques. Patients were classified by three different data mining techniques, (Logistic regression, Decision tree and Neural network) and the results were assessed. Results: The data set was created using 165,073 records of 2,228 patients classification database. Main explaining variables were as follows in 3 different data mining techniques. 1) Logistic regression : age, month and section. 2) Decision tree : section, month, age and tumor. 3) Neural network : section, diagnosis, age, sex, metastasis, hospital days and month. Among these three techniques, neural network showed the best prediction power in ROC curve verification. As the result of the patient classification prediction model developed by neural network based on nurse needs, the prediction accuracy was 84.06%. Conclusion: The patient classification prediction model was developed and tested in this study using real patients data. The result can be employed for more accurate calculation of required nursing staff and effective use of labor force.

• Asian Pacific Journal of Cancer Prevention
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• 제16권18호
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• pp.8265-8270
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• 2016

Purpose: A cost-utility analysis was performed to assess the cost-utility of neoadjuvant chemotherapy regimens containing doxorubicin and cyclophosphamide (AC) versus paclitaxel and gemcitabine (PG) for locally advanced breast cancer patients in Iran. Materials and Methods: This cross-sectional study in Namazi hospital in Shiraz, in the south of Iran covered 64 breast cancer patients. According to the random numbers, the patients were divided into two groups, 32 receiving AC and 32 PG. Costs were identified and measured from a community perspective. These items included medical and non-medical direct and indirect costs. In this study, a data collection form was used. To assess the utility of the two regimens, the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core30 (EORTC QLQ-C30) was applied. Using a decision tree, we calculated the expected costs and quality adjusted life years (QALYs) for both methods; also, the incremental cost-effectiveness ratio was assessed. Results: The results of the decision tree showed that in the AC arm, the expected cost was 39,170 US$ and the expected QALY was 3.39 and in the PG arm, the expected cost was 43,336 dollars and the expected QALY was 2.64. Sensitivity analysis showed the cost effectiveness of the AC and ICER=-5535 US$. Conclusions: Overall, the results showed that AC to be superior to PG in treatment of patients with breast cancer, being less costly and more effective.

• 의료ㆍ복지 건축 : 한국의료복지건축학회 논문집
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• 제23권3호
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• pp.57-69
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• 2017

Purpose: This study aims to emphasize interrelation between healthcare policies, design standards and hospital architecture of the United States since 1950s; to examine spatial characteristics of inpatient care facilities through case studies; and to consider the social implication of these spatial changes. Methods: In this study, reviewing the overall healthcare system, design standards and inpatient care facilities of the United States since 1950s, a total of five inpatient care facilities, one for each period, were selected in order to analyze the spatial characteristics. The spatial maps of Space Syntax were employed for analyzing five case studies. Results: The distance between the nursing station, the support service, and inpatient room were getting closer. The spatial structure of inpatient care facilities is transformed from tree structures to annular tree structures. This result shows that the efficiency between patient, staff and support service is higher and the depth of the spaces is getting deeper, which indicates that efficiency for improving healthcare quality affect the spatial structure of inpatient care facilities. Implications: In the future, if Korea's health policy is changed to a demand-oriented health care policy, this conclusion predicts medical planning of hospital will be focused on the efficiency.

• 한국정보통신학회논문지
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• 제25권10호
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• pp.1296-1301
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• 2021

최근 전 세계적으로 당뇨병 유발률이 증가함에 따라 다양한 머신러닝과 딥러닝 기술을 통해 당뇨병을 예측하려고 는 연구가 이어지고 있다. 본 연구에서는 독일의 Frankfurt Hospital 데이터로 머신러닝 기법을 활용하여 당뇨병을 예측하는 모델을 제시한다. IQR(Interquartile Range) 기법을 이용한 이상치 처리와 피어슨 상관관계 분석을 적용하고 Decision Tree, Random Forest, Knn, SVM, 앙상블 기법인 XGBoost, Voting, Stacking로 모델별 당뇨병 예측 성능을 비교한다. 연구를 진행한 결과 Stacking ensemble 기법의 정확도가 98.75%로 가장 뛰어난 성능을 보였다. 따라서 해당 모델을 이용하여 현대 사회에 만연한 당뇨병을 정확히 예측하고 예방할 수 있다는 점에서 본 연구는 의의가 있다.