• Asian-Australasian Journal of Animal Sciences
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• 제29권3호
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• pp.307-314
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• 2016

Cheese is generally considered a safe and nutritious food, but foodborne illnesses linked to cheese consumption have occurred in many countries. Several microbial risk assessments related to Listeria monocytogenes, Staphylococcus aureus, and Escherichia coli infections, causing cheese-related foodborne illnesses, have been conducted. Although the assessments of microbial risk in soft and low moisture cheeses such as semi-hard and hard cheeses have been accomplished, it has been more focused on the correlations between pathogenic bacteria and soft cheese, because cheese-associated foodborne illnesses have been attributed to the consumption of soft cheeses. As a part of this microbial risk assessment, predictive models have been developed to describe the relationship between several factors (pH, Aw, starter culture, and time) and the fates of foodborne pathogens in cheese. Predictions from these studies have been used for microbial risk assessment as a part of exposure assessment. These microbial risk assessments have identified that risk increased in cheese with high moisture content, especially for raw milk cheese, but the risk can be reduced by preharvest and postharvest preventions. For accurate quantitative microbial risk assessment, more data including interventions such as curd cooking conditions (temperature and time) and ripening period should be available for predictive models developed with cheese, cheese consumption amounts and cheese intake frequency data as well as more dose-response models.

• 한국축산식품학회지
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• 제37권4호
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• pp.579-592
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• 2017

This study assessed the quantitative microbial risk of non-enterohemorrhagic Escherichia coli (EHEC). For hazard identification, hazards of non-EHEC E. coli in natural and processed cheeses were identified by research papers. Regarding exposure assessment, non-EHEC E. coli cell counts in cheese were enumerated, and the developed predictive models were used to describe the fates of non-EHEC E. coli strains in cheese during distribution and storage. In addition, data on the amounts and frequency of cheese consumption were collected from the research report of the Ministry of Food and Drug Safety. For hazard characterization, a doseresponse model for non-EHEC E. coli was used. Using the collected data, simulation models were constructed, using software @RISK to calculate the risk of illness per person per day. Non-EHEC E. coli cells in natural- (n=90) and processed-cheese samples (n=308) from factories and markets were not detected. Thus, we estimated the initial levels of contamination by Uniform distribution ${\times}$ Beta distribution, and the levels were -2.35 and -2.73 Log CFU/g for natural and processed cheese, respectively. The proposed predictive models described properly the fates of non-EHEC E. coli during distribution and storage of cheese. For hazard characterization, we used the Beta-Poisson model (${\alpha}=2.21{\times}10^{-1}$, $N_{50}=6.85{\times}10^7$). The results of risk characterization for non-EHEC E. coli in natural and processed cheese were $1.36{\times}10^{-7}$ and $2.12{\times}10^{-10}$ (the mean probability of illness per person per day), respectively. These results indicate that the risk of non-EHEC E. coli foodborne illness can be considered low in present conditions.

• Journal of the Korean Data and Information Science Society
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• 제26권6호
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• pp.1417-1426
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• 2015

본 연구는 한국장학재단의 2012-2014년간 일반 학자금 대출 자료를 활용하여 부실채권 보유 및 신용유의자로 분류될 수 있는 위험요인들을 파악하고, 부실 고위험군 예측모형을 개발했다. 예측모형 개발은 데이터마이닝 방법 중 의사결정나무 분석을 적용하였으며, 분석 패키지는 SAS Enterprise Miner 13.2를 활용했다. 개발된 모형은 25가지의 그룹으로 세분화 했으며, 부실 위험군에 영향을 미치는 주요 요인은 소득분위, 국가장학금 수혜유무, 나이, 연체계좌 보유 이력, 대학구분 (학부/대학원), 전공 계열, 월평균 상환액이 주요 요인으로 나타났다. 본 연구에서 개발된 부실 고위험군 예측모형은 장기연체로 인한 부실채권 발생 및 신용유의자 발생 예방을 위한 세분화된 관리서비스 제공을 위한 기초자료가 될 수 있을 것이다.

• Journal of Trauma and Injury
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• 제37권2호
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• pp.124-131
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• 2024

Purpose: The aim of this study was to utilize the American College of Surgeons Trauma Quality Improvement Program (TQIP) database to identify risk factors associated with developing acute compartment syndrome (ACS) following lower extremity fractures. Specifically, a nomogram of variables was constructed in order to propose a risk calculator for ACS following lower extremity trauma. Methods: A large retrospective case-control study was conducted using the TQIP database to identify risk factors associated with developing ACS following lower extremity fractures. Multivariable regression was used to identify significant risk factors and subsequently, these variables were implemented in a nomogram to develop a predictive model for developing ACS. Results: Novel risk factors identified include venous thromboembolism prophylaxis type particularly unfractionated heparin (odds ratio [OR], 2.67; 95% confidence interval [CI], 2.33-3.05; P<0.001), blood product transfusions (blood per unit: OR 1.13 [95% CI, 1.09-1.18], P<0.001; platelets per unit: OR 1.16 [95% CI, 1.09-1.24], P<0.001; cryoprecipitate per unit: OR 1.13 [95% CI, 1.04-1.22], P=0.003). Conclusions: This study provides evidence to believe that heparin use and blood product transfusions may be additional risk factors to evaluate when considering methods of risk stratification of lower extremity ACS. We propose a risk calculator using previously elucidated risk factors, as well as the risk factors demonstrated in this study. Our nomogram-based risk calculator is a tool that will aid in screening for high-risk patients for ACS and help in clinical decision-making.

• 한국심리학회지:법
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• 제13권2호
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• pp.99-119
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• 2022

성범죄자의 재범 우려가 큰 가운데 특정 성범죄자들은 다른 성범죄자들에 비해 재범하는 경향이 더 높은 것으로 알려져 있다. 이에, 연구자들은 성범죄자의 재범을 예측하는 요인들을 규명하기 위해 노력해왔다. 본 연구에서는 현재 교정시설에서 사용하는 STATIC-99(정적 위험 요인 평가)와 HAGSOR-동적요인을 사용하여, 각 도구의 재범 예측력을 분석하였다. 분석 결과 STATIC-99와 HAGSOR-동적요인은 통계적으로 유의한 재범 예측력을 가진 것으로 나타났다(각각 AUC = .737, AUC = .597, ps < .001). 그러나 성범죄 재범에 대해서는 STATIC-99만 통계적으로 유의한 예측력을 보였다(AUC = .743, p < .001). 또한 HAGSOR-동적요인을 추가한 Model의 재범 예측에 대한 설명력은 STATIC-99만을 투입한 Model보다 통계적으로 유의한 수준에서 증가하는 것으로 나타나(∆χ² = 12.721, p < .001), HAGSOR-동적요인의 증분적 재범 예측력이 확인되었다. 그러나 성범죄에 대한 증분적 재범 예측력은 확인되지 못하였다. 재범 위험성 평가 항목 중 정적 위험요인은 친족피해자를 제외한 모든 항목이 재범을 유의하게 예측하였으며, 동적 위험요인 중 범죄적 성격, 대인관계 공격성, 사회적지지(부족)이 재범에 영향을 미치는 것으로 분석되었다. 결과에 대한 함의와 현장에서 도구의 사용 문제, 개선 방향 등이 추가로 논의되었다.

• Asian Pacific Journal of Cancer Prevention
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• 제17권1호
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• pp.347-352
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• 2016

Background: This study used receiver operating characteristic curve to analyze Surveillance, Epidemiology and End Results (SEER) adenosquamous carcinoma data to identify predictive models and potential disparities in outcome. Materials and Methods: This study analyzed socio-economic, staging and treatment factors available in the SEER database for adenosquamous carcinoma. For the risk modeling, each factor was fitted by a generalized linear model to predict the cause specific survival. An area under the receiver operating characteristic curve (ROC) was computed. Similar strata were combined to construct the most parsimonious models. Results: A total of 20,712 patients diagnosed from 1973 to 2009 were included in this study. The mean follow up time (S.D.) was 54.2 (78.4) months. Some 2/3 of the patients were female. The mean (S.D.) age was 63 (13.8) years. SEER stage was the most predictive factor of outcome (ROC area of 0.71). 13.9% of the patients were un-staged and had risk of cause specific death of 61.3% that was higher than the 45.3% risk for the regional disease and lower than the 70.3% for metastatic disease. Sex, site, radiotherapy, and surgery had ROC areas of about 0.55-0.65. Rural residence and race contributed to socioeconomic disparity for treatment outcome. Radiotherapy was underused even with localized and regional stages when the intent was curative. This under use was most pronounced in older patients. Conclusions: Anatomic stage was predictive and useful in treatment selection. Under-staging may have contributed to poor outcome.

• Journal of the Korean Data and Information Science Society
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• 제28권6호
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• pp.1337-1348
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• 2017

본 연구는 질병관리본부에서 실시한 전국 규모의 자료인 지역사회건강조사 2014년 자료를 이용하여 고위험 음주자들의 특성 및 요인을 파악하고 고위험 음주 예측모형을 개발했다. 예측모형 개발은 데이터마이닝 방법 중 로지스틱 회귀분석, 의사결정나무, 신경망 분석 3가지 방법을 적용했으며, 로지스틱 회귀분석의 주요 결과로는 40대 남자의 위험도가 높았고, 사무직과 판매서비스직의 위험도가 높았다. 특히 현재 흡연자인 경우 고위험 음주 위험도가 높았다. 3가지 방법 중 AUROC (area under a receiver operation characteristic curve) 측면에서 신경망 분석과 로지스틱 회귀분석이 가장 높게 나타났다. 또한 고위험 음주 예방을 위한 우선 관리 대상자를 선정함에 있어 신경망 분석과 로지스틱 회귀분석으로 개발된 예측모형의 사후확률을 기초로 두 가지 모형 모두 예측분포의 상위 10%인 집단에 해당되는 경우를 선정한 결과 신경망 분석이나 로지스틱 회귀모형 1가지 모형으로 적용하는 것보다 반응률 및 향상도가 다소 개선되는 것으로 나타났다. 본 연구에서 개발된 고위험 음주 예측모형과 우선 관리 대상자 선정 방법은 문제적 음주 예방 및 개선 교육, 절주 프로그램 개발 등에 보다 세분화되고 효과적인 건강관리 서비스를 제공을 위한 기초자료가 될 수 있을 것이다.

• 한국측량학회지
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• 제39권6호
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• pp.351-361
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• 2021

화재는 막대한 재산과 인명피해를 초래하고 있으며 크고 작은 화재가 지속해서 발생하고 있다. 따라서 본 연구는 화재 유형별로 화재에 영향을 미치는 각종 위험요인을 예측하고자 한다. 전국에서 화재 발생 건수가 가장 많은 경기도를 대상으로 화재발생위험요인 예측분석을 실시하였다. 또한, 머신러닝 방법인 SVM, RF, GBRT를 활용하여 각 모형의 정확성을 MAE,RMSE를 통해 적합도가 높은 모형을 제시하였으며 이를 토대로 경기도 화재발생요인 예측분석을 실시하였다. 머신러닝 방법 3가지를 비교분석한 결과 RF가 MAE 1.517, RMSE 1.820으로 나타났으며 MAE, RMSE 검증데이터 및 시험데이터의 경우 MAE값 0.024, RMSE값 0.12의 차이로 매우 유사하게 나타나 가장 우수한 예측력으로 나타났다. RF기법을 적용하여 분석한 결과 공통적으로 발화장소가 화재발생에 가장 큰 영향을 주는 위험요인으로 나타났다. 이러한 연구 결과는 화재발생에 영향을 주는 요인들의 위험순서를 파악하여 화재안전관리의 유용한 자료로 활용될 것으로 예상된다.

• Asian Pacific Journal of Cancer Prevention
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• 제14권3호
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• pp.1769-1772
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• 2013

A case-control study of the association of miR-499A>G rs3746444 with risk of hepatocellular carcinoma (HCC)was conducted. Patients with HCC and healthy control subjects were recruited for genotyping of miR-499A>G using duplex polymerase-chain-reaction with confronting-two-pair primer(PCR-RFLP) analysis. The MiR-499 GG genotype was associated with a decreased risk of HCC as compared with the miR-499 AA genotype (adjusted OR=0.74, 95%CI=0.24-0.96). Similarly, the GG genotype showed a 0.45-fold decreased HCC risk in a recessive model. The MiR-499 G allele was significantly associated with decreased risk of HCC among patients infected with HBV in a dominant model (OR=0.09, 95%CI= 0.02-0.29). In conclusion, the MiR-499A>G rs3746444 polymorphism is associated with HCC risk in the Chinese population, and may be useful predictive marker for CAD susceptibility.

• 대한간호학회지
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• 제49권5호
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• pp.575-585
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• 2019

Purpose: The purpose of this study was to develop predictive models for pressure ulcer incidence using electronic health record (EHR) data and to compare their predictive validity performance indicators with that of the Braden Scale used in the study hospital. Methods: A retrospective case-control study was conducted in a tertiary teaching hospital in Korea. Data of 202 pressure ulcer patients and 14,705 non-pressure ulcer patients admitted between January 2015 and May 2016 were extracted from the EHRs. Three predictive models for pressure ulcer incidence were developed using logistic regression, Cox proportional hazards regression, and decision tree modeling. The predictive validity performance indicators of the three models were compared with those of the Braden Scale. Results: The logistic regression model was most efficient with a high area under the receiver operating characteristics curve (AUC) estimate of 0.97, followed by the decision tree model (AUC 0.95), Cox proportional hazards regression model (AUC 0.95), and the Braden Scale (AUC 0.82). Decreased mobility was the most significant factor in the logistic regression and Cox proportional hazards models, and the endotracheal tube was the most important factor in the decision tree model. Conclusion: Predictive validity performance indicators of the Braden Scale were lower than those of the logistic regression, Cox proportional hazards regression, and decision tree models. The models developed in this study can be used to develop a clinical decision support system that automatically assesses risk for pressure ulcers to aid nurses.