• 한국노년학
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• 제41권2호
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• pp.253-270
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• 2021

본 연구는 세대 간 거주근접성이 성인자녀가 중고령부모에게 제공하는 경제적 자원이전과 어떠한 관련성이 있는지, 세대간 거주근접성과 경제적 자원이전의 관계가 자녀의 성별에 따라 차이가 나타나는지 살펴보았다. 자녀 본인 뿐 아니라 형제자매의 거주근접성 역시 고려되었다. 미국 등 국외 선행연구는 근거리에 거주하는 형제자매가 제공하는 도구적 지원을 보완하기 위해 원거리에 거주하는 자녀의 경제적 지원수준이 높을 것으로 보았다. 데이터는 한국고령화연구패널 5차(2014)자료를 활용하였으며 연구대상은 부모의 연령이 60세 이상이고 자녀의 연령은 35세에서 55세 사이로 부모가 자녀와 동거하는 경우 분석에서 제외되었다. 부모의 경우 부부가구이거나 배우자와 사별한 독거가구로 구분하였다. 고정효과모형과 확률효과모형으로 가족내 분석을 실시하였고 연구결과는 다음과 같다. 첫째, 부모와 30분 이내 거리에 거주하거나 혹은 1시간 이상 2시간 이내 거리에 거주하는 경우 아들의 경제적 지원액이 딸보다 많았다. 둘째, 선행연구 결과와 달리, 형제자매를 포함하여 모든 자녀가 부모와 1시간 이상 거리에 거주할 때 아들과 딸 성별의 구분 없이 경제적 지원액이 높았다. 세대간 거주근접성과 경제적 자원이전의 관련성은 배우자와 사별한 1인가구 부모가 경제적 지원의 수혜자인 경우 나타나지 않았다.

• 동의신경정신과학회지
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• 제34권2호
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• pp.71-82
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• 2023

Objectives: The existing reductionist approach has not reached complete understanding of the cause of schizophrenia. The objective of this study was to investigate yin-yang and five-element characteristics reflected on four time pillars of birth of patients with schizophrenia through comparison with the general population in the perspective of consilience-based holistic approach. Methods: This study was conducted using a random sequential recruitment method for the general population and individuals with schizophrenia aged 18 to 64 based on the exact date and time of birth using structured questionnaires. Relative positional relations of yin-yang and five-element with day master were primarily examined. In addition, the strength of day master with a score range of 0~100 points was assessed through operational score allocation. Results: Of 591 participants, 576 (346: general population, 230: individuals with schizophrenia) were analyzed. Between-group analyses showed no significant difference in the distribution of types of day master (χ²=10.41, df=9, p=0.318). However, significant between-group differences were shown in the distribution of the strength of day master (t=2.14, p=0.032) and frequency of restraining month branch (χ²=5.23, df=1, p=0.022). In logistic regression analysis, 10-point increase on the strength of day master decreased the probability of onset of schizophrenia over the age of 30 by 29.6% (p=0.002; 95% confidence interval, 0.566~0.876). Conclusions: Findings in this study suggest that four time pillars of birth might be associated with schizophrenia through yin-yang and five-element theory and synchronicity principle, implicating the plausibility of consilience-based holistic approach in the determination of risk factors or cause of schizophrenia.

• 한국수자원학회논문집
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• 제55권4호
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• pp.279-289
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• 2022

기후변화로 집중호우의 강도가 증가함에 따라 하천에서 기왕의 관측 자료가 없는 고수위가 관측되고 있다. 수위-유량 관계 곡선식은 기왕 관측 자료를 바탕으로 수위를 유량으로 환산하기 때문에 관측 자료가 부족한 경우, 유량 자료 생산에 있어서 불확실성이 커지고 정확성이 떨어지게 된다. 부족한 자료를 보완하고 유량자료의 정확성을 높이기 위하여 본 연구에서는 1차원 수치해석 모형을 기반으로 한 Monte Carlo 모의를 이용하여 대상 지역의 유량을 수리학적으로 추정하는 방법을 제시하였다. 기존에 작성되어있는 수위-유량 관계곡선식을 바탕으로 경사, 조도계수, 하폭 등의 영향을 받는 계수와 흐름의 조건에 따라 영향을 받는 지수를 난수로 발생시켜 다수의 가상 곡선식을 생성하였다. 가상의 곡선식과 주요지점의 관측수위를 활용한 수치모의 결과를 비교하여 홍수위의 재현성이 좋은 최적 샘플의 곡선식을 상류 경계 지점의 곡선식으로 선정하였다. 제안한 방법론을 섬진강 요천 합류부를 대상으로 적용하였으며, 그 결과 수위 재현성이 큰 폭으로 개선된 것을 확인하였다. 또한, 제안한 방법론의 적용 시 해당 샘플의 수리해석 결과를 바탕으로 모의단면의 수위와 유량을 수리학적으로 추정할 수 있다는 장점이 있다.

• 예술인문사회 융합 멀티미디어 논문지
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• 제6권8호
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• pp.63-72
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• 2016

본 연구의 주목적은 자아존중감과 청소년 외현화 문제행동 간의 영향분석과 아버지애착이 조절효과를 통하여 청소년 외현화 문제행동을 감소시키는 요인으로 작용하는지를 실증적으로 검증하고자 한다. 연구대상은 D지역의 남자 고등학생을 대상으로 무작위표집방법을 활용하여 연구자가 학교를 직접 방문하여 남자 고등학생 336명에게 설문조사를 실시하였다. 설문조사 결과, 무성의 설문지 38부를 제외하고 최종 298부를 분석에 활용하였다. SPSS 21.0을 사용하여 변수 간의 관계에 관한 기초적인 판단자료로 활용하기 위해 단순상관관계분석을 실시한 후, 상호작용모형을 알아보기 위하여 위계적중다회귀분석을 실시하였다. 연구결과, 자아존중감과 아버지애착이 청소년의 외현화 문제행동에 통계적으로 유의한 영향을 미치는 것으로 나타났으며, 아버지애착은 자아존중감과 외현화 문제행동의 관계에서 조절하는 효과가 나타났다. 이러한 결과를 통하여 청소년들의 자아존중감 향상 방안과 아버지와의 애착 증진 방안을 통하여 외현화 문제행동을 줄이기 위한 제언을 하고자 한다.

• 한국컴퓨터정보학회논문지
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• 제28권11호
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• pp.1-11
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• 2023

유방암은 전 세계적으로 여성들 대다수에게 가장 두려워하는 질환이다. 오늘날 데이터의 증가와 컴퓨팅 기술의 향상으로 머신러닝(machine learning)의 효율성이 증대되어 암 검출 및 진단 등에 중요한 역할을 하고 있다. 딥러닝(deep learning)은 인공신경망(artificial neural network, ANN)을 기반으로 하는 머신러닝 기술의 한 분야로 최근 여러 분야에서 성능이 급속도로 개선되어 활용 범위가 확대되고 있다. 본 연구에서는 유방암 분류를 위해 전이학습(transfer learning) 기반 DNN(Deep Neural Network)과 SVM(support vector machine)의 구조를 결합한 DNN-SVM Hybrid 모형을 제안한다. 전이학습 기반 제안된 모형은 적은 학습 데이터에도 효과적이고, 학습 속도도 빠르며, 단일모형, 즉 DNN과 SVM이 가지는 장점을 모두 활용 가능토록 결합함으로써 모형 성능이 개선되었다. 제안된 DNN-SVM Hybrid 모형의 성능평가를 위해 UCI 머신러닝 저장소에서 제공하는 WOBC와 WDBC 유방암 자료를 가지고 성능실험 결과, 제안된 모형은 여러 가지 성능 척도 면에서 단일모형인 로지스틱회귀 모형, DNN, SVM 그리고 앙상블 모형인 랜덤 포레스트보다 우수함을 보였다.

• 벤처창업연구
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• 제17권1호
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• pp.229-249
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• 2022

본 연구는 다양한 머신러닝 기법을 통해 코스닥(KOSDAQ) 시장 내 관리종목 지정을 예측할 수 있는 모델에 대해 연구하였다. 증권시장 내 기업이 관리종목으로 지정이 되면 시장에서는 이를 부정적인 정보로 인식하여 해당 기업과 투자자에게 손실을 가져오게 된다. 본 연구를 통해 기업의 재무적 데이터를 바탕으로 조기에 관리종목 지정을 예측하고, 투자자들의 포트폴리오 리스크 관리에 도움을 주기 위한 머신러닝 접근이 타당한지 살펴본다. 본 연구를 위해 활용한 독립변수는 수익성, 안정성, 활동성, 성장성을 나타내는 21개의 재무비율을 활용하였으며, K-IFRS가 적용된 2011년부터 2020년까지 관리종목과 비관리종목의 기업의 재무 데이터를 표본으로 추출하였다. 로지스틱 회귀분석, 의사결정나무, 서포트 벡터 머신, 랜덤 포레스트, LightGBM을 활용하여 관리종목 지정 예측 연구를 수행하였다. 연구결과는 분류 정확도가 82.73%인 LightGBM이 가장 우수한 예측 모형이었으며 분류 정확도가 가장 낮은 예측 모형은 정확도가 71.94%인 의사결정나무였다. 의사결정나무 기반 학습 모형의 변수 중요도의 상위 3개 변수를 확인한 결과 각 모형에서 공통적으로 나온 재무변수는 ROE(당기순이익), 자본금회전율(Capital stock turnover ratio)로 해당 재무변수가 관리종목 지정에 있어 상대적으로 중요한 변수임을 확인하였다. 대체적으로 앙상블을 이용한 학습 모형이 단일 학습 모형보다 예측 성능이 높은 것을 확인하였다. 기존 선행연구가 K-IFRS에 대한 고려를 하지 않았고, 다소 제한된 머신러닝에 의존하였다. 따라서 본 연구의 필요성과 함께 현실적 요구를 충족시키는 결과를 제시하였음을 알 수 있으며, 시장참여자들에게 있어 관리종목 지정에 대한 사전 예측을 확인할 수 있도록 기여했다고 볼 수 있다.

• Korean Journal of Radiology
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• 제22권12호
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• pp.2082-2093
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• 2021

Objective: We conducted a systematic review and meta-analysis of the tissue adequacy and complication rates of percutaneous transthoracic needle biopsy (PTNB) for molecular analysis in patients with non-small cell lung cancer (NSCLC). Materials and Methods: We performed a literature search of the OVID-MEDLINE and Embase databases to identify original studies on the tissue adequacy and complication rates of PTNB for molecular analysis in patients with NSCLC published between January 2005 and January 2020. Inverse variance and random-effects models were used to evaluate and acquire meta-analytic estimates of the outcomes. To explore heterogeneity across the studies, univariable and multivariable metaregression analyses were performed. Results: A total of 21 studies with 2232 biopsies (initial biopsy, 8 studies; rebiopsy after therapy, 13 studies) were included. The pooled rates of tissue adequacy and complications were 89.3% (95% confidence interval [CI]: 85.6%-92.6%; I² = 0.81) and 17.3% (95% CI: 12.1%-23.1%; I² = 0.89), respectively. These rates were 93.5% and 22.2% for the initial biopsies and 86.2% and 16.8% for the rebiopsies, respectively. Severe complications, including pneumothorax requiring chest tube placement and massive hemoptysis, occurred in 0.7% of the cases (95% CI: 0%-2.2%; I² = 0.67). Multivariable meta-regression analysis showed that the tissue adequacy rate was not significantly lower in studies on rebiopsies (p = 0.058). The complication rate was significantly higher in studies that preferentially included older adults (p = 0.001). Conclusion: PTNB demonstrated an average tissue adequacy rate of 89.3% for molecular analysis in patients with NSCLC, with a complication rate of 17.3%. PTNB is a generally safe and effective diagnostic procedure for obtaining tissue samples for molecular analysis in NSCLC. Rebiopsy may be performed actively with an acceptable risk of complications if clinically required.

• Korean Journal of Radiology
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• 제22권3호
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• pp.334-343
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• 2021

Objective: We aimed to develop a prediction model for diagnosing severe aortic stenosis (AS) using computed tomography (CT) radiomics features of aortic valve calcium (AVC) and machine learning (ML) algorithms. Materials and Methods: We retrospectively enrolled 408 patients who underwent cardiac CT between March 2010 and August 2017 and had echocardiographic examinations (240 patients with severe AS on echocardiography [the severe AS group] and 168 patients without severe AS [the non-severe AS group]). Data were divided into a training set (312 patients) and a validation set (96 patients). Using non-contrast-enhanced cardiac CT scans, AVC was segmented, and 128 radiomics features for AVC were extracted. After feature selection was performed with three ML algorithms (least absolute shrinkage and selection operator [LASSO], random forests [RFs], and eXtreme Gradient Boosting [XGBoost]), model classifiers for diagnosing severe AS on echocardiography were developed in combination with three different model classifier methods (logistic regression, RF, and XGBoost). The performance (c-index) of each radiomics prediction model was compared with predictions based on AVC volume and score. Results: The radiomics scores derived from LASSO were significantly different between the severe AS and non-severe AS groups in the validation set (median, 1.563 vs. 0.197, respectively, p < 0.001). A radiomics prediction model based on feature selection by LASSO + model classifier by XGBoost showed the highest c-index of 0.921 (95% confidence interval [CI], 0.869-0.973) in the validation set. Compared to prediction models based on AVC volume and score (c-indexes of 0.894 [95% CI, 0.815-0.948] and 0.899 [95% CI, 0.820-0.951], respectively), eight and three of the nine radiomics prediction models showed higher discrimination abilities for severe AS. However, the differences were not statistically significant (p > 0.05 for all). Conclusion: Models based on the radiomics features of AVC and ML algorithms may perform well for diagnosing severe AS, but the added value compared to AVC volume and score should be investigated further.

• Korean Journal of Radiology
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• 제22권1호
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• pp.106-117
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• 2021

Objective: To explore the value of magnetic resonance imaging (MRI)-based whole tumor texture analysis in differentiating borderline epithelial ovarian tumors (BEOTs) from FIGO stage I/II malignant epithelial ovarian tumors (MEOTs). Materials and Methods: A total of 88 patients with histopathologically confirmed ovarian epithelial tumors after surgical resection, including 30 BEOT and 58 MEOT patients, were divided into a training group (n = 62) and a test group (n = 26). The clinical and conventional MRI features were retrospectively reviewed. The texture features of tumors, based on T2-weighted imaging, diffusion-weighted imaging, and contrast-enhanced T1-weighted imaging, were extracted using MaZda software and the three top weighted texture features were selected by using the Random Forest algorithm. A non-texture logistic regression model in the training group was built to include those clinical and conventional MRI variables with p value < 0.10. Subsequently, a combined model integrating non-texture information and texture features was built for the training group. The model, evaluated using patients in the training group, was then applied to patients in the test group. Finally, receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of the models. Results: The combined model showed superior performance in categorizing BEOTs and MEOTs (sensitivity, 92.5%; specificity, 86.4%; accuracy, 90.3%; area under the ROC curve [AUC], 0.962) than the non-texture model (sensitivity, 78.3%; specificity, 84.6%; accuracy, 82.3%; AUC, 0.818). The AUCs were statistically different (p value = 0.038). In the test group, the AUCs, sensitivity, specificity, and accuracy were 0.840, 73.3%, 90.1%, and 80.8% when the non-texture model was used and 0.896, 75.0%, 94.0%, and 88.5% when the combined model was used. Conclusion: MRI-based texture features combined with clinical and conventional MRI features may assist in differentitating between BEOT and FIGO stage I/II MEOT patients.

• 한국병원경영학회지
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• 제9권1호
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• pp.66-94
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• 2004

The objective of this study is to examine how to maximize the efficiency of hospital management by minimizing the unit cost of hospital operation. For this purpose, this paper proposes to develop a model of the profit maximization based on the cost minimization dictum using the statistical tools of arriving at the maximum likelihood values. The preliminary survey data are collected from the annual statistics and their analyses published by Korea Health Industry Development Institute and Korean Hospital Association. The maximum likelihood value statistical analyses are conducted from the information on the cost (function) of each of 36 hospitals selected by the random stratified sampling method according to the size and location (urban or rural) of hospitals. We believe that, although the size of sample is relatively small, because of the sampling method used and the high response rate, the power of estimation of the results of the statistical analyses of the sample hospitals is acceptable. The conceptual framework of analyses is adopted from the various models of the determinants of hospital costs used by the previous studies. According to this framework, the study postulates that the unit cost of hospital operation is determined by the size, scope of service, technology (production function) as measured by capacity utilization, labor capital ratio and labor input-mix variables, and by exogeneous variables. The variables to represent the above cost determinants are selected by using the step-wise regression so that only the statistically significant variables may be utilized in analyzing how these variables impact on the hospital unit cost. The results of the analyses show that the models of hospital cost determinants adopted are well chosen. The various models analyzed have the (goodness of fit) overall determination (R2) which all turned out to be significant, regardless of the variables put in to represent the cost determinants. Specifically, the size and scope of service, no matter how it is measured, i. e., number of admissions per bed, number of ambulatory visits per bed, adjusted inpatient days and adjusted outpatients, have overall effects of reducing the hospital unit costs as measured by the cost per admission, per inpatient day, or office visit implying the existence of the economy of scale in the hospital operation. Thirdly, the technology used in operating a hospital has turned out to have its ramifications on the hospital unit cost similar to those postulated in the static theory of the firm. For example, the capacity utilization as represented by the inpatient days per employee tuned out to have statistically significant negative impacts on the unit cost of hospital operation, while payroll expenses per inpatient cost has a positive effect. The input-mix of hospital operation, as represented by the ratio of the number of doctor, nurse or medical staff per general employee, supports the known thesis that the specialized manpower costs more than the general employees. The labor/capital ratio as represented by the employees per 100 beds is shown to have a positive effect on the cost as expected. As for the exogeneous variable's impacts on the cost, when this variable is represented by the percent of urban 100 population at the location where the hospital is located, the regression analysis shows that the hospitals located in the urban area have a higher cost than those in the rural area. Finally, the case study of the sample hospitals offers a specific information to hospital administrators about how they share in terms of the cost they are incurring in comparison to other hospitals. For example, if his/her hospital is of small size and located in a city, he/she can compare the various costs of his/her hospital operation with those of other similar hospitals. Therefore, he/she may be able to find the reasons why the cost of his/her hospital operation has a higher or lower cost than other similar hospitals in what factors of the hospital cost determinants.