• 동의생리병리학회지
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• 제25권1호
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• pp.138-143
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• 2011

In spite of abundant clinical resources of stroke patients, the objective and logical data analyses or diagnostic systems were not established in oriental medicine. As a part of researches for standardization and objectification of differentiation of syndromes for stroke, in this present study, we tried to develop the statistical diagnostic tool discriminating the 4 subtypes of syndrome differentiation using the essential indices considering the sex. Discriminant analysis was carried out using clinical data collected from 1,448 stroke patients who was identically diagnosed for the syndrome differentiation subtypes diagnosed by two clinical experts with more than 3 year experiences. Empirical discriminant model(V) for different sex was constructed using 61 significant symptoms and sign indices selected by stepwise selection. We comparison. We make comparison a between discriminant model(V) and discriminant model(IV) using 33 significant symptoms and sign indices selected by stepwise selection. Development of statistical diagnostic tool discriminating 4 subtypes by sex : The discriminant model with the 24 significant indices in women and the 19 significant indices in men was developed for discriminating the 4 subtypes of syndrome differentiation including phlegm-dampness, qi-deficiency, yin-deficiency and fire-heat. Diagnostic accuracy and prediction rate of syndrome differentiation by sex : The overall diagnostic accuracy and prediction rate of 4 syndrome differentiation subtypes using 24 symptom and sign indices was 74.63%(403/540) and 68.46%(89/130) in women, 19 symptom and sign indices was 72.05%(446/619) and 70.44%(112/159) in men. These results are almost same as those of that the overall diagnostic accuracy(73.68%) and prediction rate(70.59%) are analyzed by the discriminant model(IV) using 33 symptom and sign indices selected by stepwise selection. Considering sex, the statistical discriminant model(V) with significant 24 symptom and sign indices in women and 19 symptom and sign indices in men, instead of 33 indices would be used in the field of oriental medicine contributing to the objectification of syndrome differentiation with parsimony rule.

• Korean Journal of Radiology
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• 제21권9호
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• pp.1055-1064
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• 2020

Objective: To assess the incremental prognostic value of coronary computed tomography angiography (CCTA) in comparison to a clinical risk model (Framingham risk score, FRS) and coronary artery calcium score (CACS) for future cardiac events in ischemic stroke patients without chest pain. Materials and Methods: This retrospective study included 1418 patients with acute stroke who had no previous cardiac disease and underwent CCTA, including CACS. Stenosis degree and plaque types (high-risk, non-calcified, mixed, or calcified plaques) were assessed as CCTA variables. High-risk plaque was defined when at least two of the following characteristics were observed: low-density plaque, positive remodeling, spotty calcification, or napkin-ring sign. We compared the incremental prognostic value of CCTA for major adverse cardiovascular events (MACE) over CACS and FRS. Results: The prevalence of any plaque and obstructive coronary artery disease (CAD) (stenosis ≥ 50%) were 70.7% and 30.2%, respectively. During the median follow-up period of 48 months, 108 patients (7.6%) experienced MACE. Increasing FRS, CACS, and stenosis degree were positively associated with MACE (all p < 0.05). Patients with high-risk plaque type showed the highest incidence of MACE, followed by non-calcified, mixed, and calcified plaque, respectively (log-rank p < 0.001). Among the prediction models for MACE, adding stenosis degree to FRS showed better discrimination and risk reclassification compared to FRS or the FRS + CACS model (all p < 0.05). Furthermore, incorporating plaque type in the prediction model significantly improved reclassification (integrated discrimination improvement, 0.08; p = 0.023) and showed the highest discrimination index (C-statistics, 0.85). However, the addition of CACS on CCTA with FRS did not add to the prediction ability for MACE (p > 0.05). Conclusion: Assessment of stenosis degree and plaque type using CCTA provided additional prognostic value over CACS and FRS to risk stratify stroke patients without prior history of CAD better.

• 동의생리병리학회지
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• 제23권6호
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• pp.1460-1464
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• 2009

In spite of many studies on statistical model for pattern identifications (PIs), little attention has been paid to the complexity of pattern diagnosis processed by oriental physicians. The aim of this study is to develop a statistical diagnostic model which discriminates four PIs using multiple indicators in stroke. Clinical data were collected from 981 stroke patients and 516 data of which PIs were agreed by two independent physicians were included. Discriminant analysis was carried out using clinical indicators such as symptoms and signs which referred to pattern diagnosis, and applied to validation samples which contained all symptoms and signs manifested. Four Fischer's linear discriminant models were derived and their accuracy and prediction rates were 93.2% and 80.43%, respectively. It is important to consider the pattern diagnosis processed by oriental physicians in developing statistical model for PIs. The discriminant model developed in this study using multiple indicators is valid, and can be used in the clinical fields.

• 대한토목학회논문집
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• 제41권3호
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• pp.317-325
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• 2021

1988년 부산에서 처음 발병된 소나무재선충병(Pine Wilt Disease, PWD)은 우리나라 소나무에 막대한 피해를 주고 있는 심각한 질병이다. 정부에서는 2005년 소나무재선충병 방제특별법을 제정하고 피해지역의 소나무 이동 금지와 방제를 시행하고 있다. 하지만, 기존의 예찰 및 방제방법은 산악지형에서 동시다발적이고 급진적으로 발생하는 소나무재선충병을 줄이기에는 물리적, 경제적 어려움이 있다. 따라서 본 연구에서는 소나무재선충병 감염의심목을 효율적으로 탐지하기 위해 무인항공기를 이용한 영상자료를 바탕으로 딥러닝 객체인식 예찰 방법의 활용가능성을 제시하고자 한다. 소나무재선충병 피해목을 관측하기 위해서 항공촬영을 통해 영상 데이터를 획득하고 정사영상을 제작하였다. 그 결과 198개의 피해목이 확인되었으며, 이를 검증하기 위해서 접근이 불가한 급경사지나 절벽과 같은 곳을 제외하고 현장 조사를 진행하여 84개의 피해목을 확인할 수 있었다. 검증된 데이터를 가지고 분할방법인 SegNet과 검출방법인 YOLOv2를 이용하여 분석한 결과 성능은 각각 0.57, 0.77로 나타났다.

• Journal of Preventive Medicine and Public Health
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• 제55권4호
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• pp.313-320
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• 2022

The KoreaN Cohort Study for Outcomes in Patients With Chronic Kidney Disease (KNOW-CKD) was launched in 2011 with the support of the Korea Disease Control and Prevention Agency. The study was designed with the aim of exploring the various clinical features and characteristics of chronic kidney disease (CKD) in Koreans, and elucidating the risk factors for CKD progression and adverse outcomes of CKD. For the cohort study, nephrologists at 9 tertiary university-affiliated hospitals participated in patient recruitment and follow-up. Biostatisticians and epidemiologists also participated in the basic design and structuring of the study. From 2011 until 2016, the KNOW-CKD Phase I recruited 2238 adult patients with CKD from stages G1 to G5, who were not receiving renal replacement therapy. The KNOW-CKD Phase II recruitment was started in 2019, with an enrollment target of 1500 subjects, focused on diabetic nephropathy and hypertensive kidney diseases in patients with reduced kidney function who are presumed to be at a higher risk of adverse outcomes. As of 2021, the KNOW-CKD investigators have published articles in the fields of socioeconomics, quality of life, nutrition, physical activity, renal progression, cardiovascular disease and outcomes, anemia, mineral bone disease, serum and urine biomarkers, and international and inter-ethnic comparisons. The KNOW-CKD researchers will elaborate a prediction model for various outcomes of CKD such as the development of end-stage kidney disease, major adverse cardiovascular events, and death.

• 한국멀티미디어학회논문지
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• 제15권6호
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• pp.701-710
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• 2012

망막색소변성(RP: Retinitis Pigmentosa)이란 가장 흔한 유전성 망막질환이다. 정상적인 사회활동을 영위하던 사람들이 이 질병으로 시력이 손상되면서 좌절과 고통을 겪는다. 또한 국가적 차원에서 이들의 경제활동이 끊김에 따라 경제활동 인구 감소에 따른 손실 또한 크다고 하겠다. 이에 망막색소변성 질환에 대한 임상 예후 정보를 제공할 수 있는 연구기반이 절실히 요구되고 있다. 본 연구는 망막색소변성 데이터에 대한 패턴 분류를 통해 예후 예측이 가능함을 제안한다. 기존에는 주로 SPSS등을 활용한 통계 처리 결과가 데이터 분석에 적용되었다. 그러나 본 연구에서는 기계학습과 자동 패턴 분류를 실험하였다. SVM(Support Vector Machine)과 여러 다양한 패턴분류기들을 실험을 위해 사용하였다. 제안한 방법은 SVM 분류기에 의하여 RP 데이터가 자동적으로 분류된 결과를 바탕으로 예후 예측이 가능함을 확인하였다.

• Genomics & Informatics
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• 제19권1호
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• pp.11.1-11.8
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• 2021

For the novel coronavirus disease 2019 (COVID-19), predictive modeling, in the literature, uses broadly susceptible exposed infected recoverd (SEIR)/SIR, agent-based, curve-fitting models. Governments and legislative bodies rely on insights from prediction models to suggest new policies and to assess the effectiveness of enforced policies. Therefore, access to accurate outbreak prediction models is essential to obtain insights into the likely spread and consequences of infectious diseases. The objective of this study is to predict the future COVID-19 situation of Korea. Here, we employed 5 models for this analysis; SEIR, local linear regression (LLR), negative binomial (NB) regression, segment Poisson, deep-learning based long short-term memory models (LSTM) and tree based gradient boosting machine (GBM). After prediction, model performance comparison was evelauated using relative mean squared errors (RMSE) for two sets of train (January 20, 2020-December 31, 2020 and January 20, 2020-January 31, 2021) and testing data (January 1, 2021-February 28, 2021 and February 1, 2021-February 28, 2021) . Except for segmented Poisson model, the other models predicted a decline in the daily confirmed cases in the country for the coming future. RMSE values' comparison showed that LLR, GBM, SEIR, NB, and LSTM respectively, performed well in the forecasting of the pandemic situation of the country. A good understanding of the epidemic dynamics would greatly enhance the control and prevention of COVID-19 and other infectious diseases. Therefore, with increasing daily confirmed cases since this year, these results could help in the pandemic response by informing decisions about planning, resource allocation, and decision concerning social distancing policies.

• 문화기술의 융합
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• 제6권4호
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• pp.289-294
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• 2020

기능성 위장장애는 Helicobacter pylori 감염 및 비 스테로이드성 항염증제의 사용 등의 원인으로 발생하는 소화기 계통 질환이다. 그동안 기능성 위장장애의 위험요인에 대한 많은 연구들이 수행되어졌으나, 한국인에 대한 기능성 위장장애 예측 모델 제시에 대한 연구는 없는 실정이다. 따라서 본 연구의 목적은 중년 및 노년층을 대상으로 인구학적정보, 비만정보, 혈액정보, 영양성분 정보를 바탕으로 머신러닝을 이용하여 기능성위장장애 예측 모델을 구현하고 평가하는 것이다. 모델생성을 위해 wrapper-based variable selection 메소드와 naive Bayes 알고리즘이 사용되었다. 여성 예측 모델의 분류 정확도는 0.712의 the area under the receiver operating characteristics curve(AUC) 값을 나타냈고, 남성에서는 여성보다 낮은 0.674의 AUC값이 나타났다. 이러한 연구결과는 향후 중년 및 노년층의 위장장애 질환의 예측과 예방에 활용될 수 있다.

• Journal of Preventive Medicine and Public Health
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• 제53권4호
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• pp.256-265
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• 2020

Objectives: We compared the associations of 3 computed tomography (CT)-based abdominal adiposity indexes with non-alcoholic fatty liver disease (NAFLD) among middle-aged Korean men and women. Methods: The participants were 1366 men and 2480 women community-dwellers aged 30-64 years. Three abdominal adiposity indexes-visceral fat area (VFA), subcutaneous fat area (SFA), and visceral-to-subcutaneous fat ratio (VSR)-were calculated from abdominal CT scans. NAFLD was determined by calculating the Liver Fat Score from comorbidities and blood tests. An NAFLD prediction model that included waist circumference (WC) as a measure of abdominal adiposity was designated as the base model, to which VFA, SFA, and VSR were added in turn. The area under the receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI), and net reclassification improvement (NRI) were calculated to quantify the additional predictive value of VFA, SFA, and VSR relative to WC. Results: VFA and VSR were positively associated with NAFLD in both genders. SFA was not significantly associated with NAFLD in men, but it was negatively associated in women. When VFA, SFA, and VSR were added to the WC-based NAFLD prediction model, the AUC improved by 0.013 (p<0.001), 0.001 (p=0.434), and 0.009 (p=0.007) in men and by 0.044 (p<0.001), 0.017 (p<0.001), and 0.046 (p<0.001) in women, respectively. The IDI and NRI were increased the most by VFA in men and VSR in women. Conclusions: Using CT-based abdominal adiposity indexes in addition to WC may improve the detection of NAFLD. The best predictive indicators were VFA in men and VSR in women.

• 한국정보통신학회논문지
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• 제21권1호
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• pp.107-114
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• 2017

홍채진단은 홍채의 패턴, 색 등 다른 특징들을 조사하여 환자의 병을 진단하는 대체의학이다. 이 논문에서는 촬영한 홍채이미지의 차영상을 이용해 홍채를 분석하고 홍채 변화에 따른 환자의 건강진단에 활용한 질병예측 알고리즘을 제안한다. 그러나 기존의 연구는 홍채영상을 이용하여 홍채 내의 특정 패턴을 검출하는 알고리즘 연구로 홍채의 다양한 정보로부터 건강 상태를 체크하는 진단시스템으로 사용하기에는 부족하다. 따라서 이 논문에서는 촬영된 홍채영상의 차영상을 이용해 질병의 조기 진단 및 질병의 전개과정을 명확히 판단한다. 또한 홍채영상으로부터 8가지 주요 홍채병소징후를 추출하고 검진의 정확도를 실험한 결과 패턴 매칭 기법에 의한 인식률 91%로 홍채진단의 자동화에 적용 가능하다.