Earticle

Health Insurance Review & Assessment Service (HIRA) claims database has a high potential to detect signals of new drug interactions. The aim of this study was to evaluate the usefulness of information component (IC) and relative risk (RR) as a tool for signal detection, and to analyze the possible drug interactions caused by clopidogrel using HIRA claims database. This study was performed in elderly patients over 65 years of age who administered clopidogrel from January 2005 to June 2006 in South Korea. Serious Adverse Events (SAEs) as drug interactions of clopidogrel were defined as any ambulatory hospitalization for ischemic diseases within comcomitant medication period of clopidogrel. Information Component (IC) and Relative Risk (RR) were calculated to compare the proportion of drug-SAE pairs in order to select drug specific SAEs. IC and RR signals of clopidogrel drug interaction were screened when IC’ 95% confidence interval was greater than 0 and RR’ 95% confidence interval was greater than 1 respectively. All detected signals were compared to references such as Micromedex® and 2010 Drug Interaction Facts™ Sensitivity, specificity, positive predicted value and negative predicted value were used to evaluate usefulness of this method. Among 13,252,930 cases of elderly patients who co-administered clopidogrel and other drugs, 47,485 cases were detected as SAE. Of these, one-hundred nine cases were detected by the IC-based data-mining approach and ninety one cases were detected by the RR-based data-mining approach. Total One-hundred sixty three unrecognized signals were detected by IC or RR. Twelve signals from IC-based data-mining (57.1%) were corresponded with drug interactions from references and eight signals from RR-based data-mining (38.1%) were corresponded with drug interactions from references. These signals include proton pump inhibitors, calcium channel blockers and HMG CoA reductase Inhibitors, which were known to affect CYP450 metabolism. Further studies using HIRA claims database are necessary to develop appropriate data-mining measure.

Big data is the compilation of enormous data that arrives from diverse sources for instance online transaction details, social media, sensor data, etc. Such assortment of enormous data develop into tough to evaluate by conventional processing relevance’s. By the development and upcoming latent in the healthcare business field, it is essential to analyze a enormous noisy data to get significant information. In healthcare system, the aim of this work is to evaluate the medical database of diabetes patients by a mixture of innovative hierarchical decision attention network, association rules (AR) and multiclass outlier classification with MapReduce framework. The association rule apriori algorithm in a MapReduce framework considers health data to create regulations. This is employed to discover the association among disease and their signs. This examination is made by means of UCI machine learning datasets of diabetes containing 50 attributes. The results of the proposed algorithm are offered by parameters for instance precision, accuracy, recall, and F-score.

CrossRef is an association of scholarly publishers that develops shared infrastructure to support more effective scholarly communications. It is a registration agency for the digital object identifier (DOI), and has built additional services for CrossRef members around the DOI and the bibliographic metadata that publishers deposit in order to register DOIs for their publications. Among these services are CrossCheck, powered by iThenticate, which helps publishers screen for plagiarism in submitted manuscripts and FundRef, which gives publishers standard way to report funding sources for published scholarly research. To add to these services, Cross-Ref launched CrossRef text and data mining services in May 2014. This article will explain the thinking behind CrossRef launching this new service, what it offers to publishers and researchers alike, how publishers can participate in it, and the uptake of the service so far.

Lane-changing represents not only a common driving behavior but also a potentially hazardous one. Accurately predicting lane change intentions plays a crucial role in enhancing road traffic safety and guiding autonomous vehicle planning. In this study, a Face-mesh model is used to extract salient features from complex driver behavior data. Subsequently, by using the Farneback optical flow algorithm in conjunction with the ResNet-50 neural network, important lane change cues were extracted from the vehicle surroundings. The Transformer model was optimized using the Teacher-forcing training strategy and the Scheduled-sampling method, fostering faster convergence and heightened prediction accuracy. Empirical tests had shown that this model had attained an impressive precision of 98.61%, recall of 98.24 %, and an F1 score of 98.42 % when forecasting lane change intentions 0.5 s ahead.

Purpose: The purpose of this study was to identify the supportive care needs of parents of preterm children in South Korea using text data from a portal site. Methods: In total, 628 online newspaper articles and 1,966 social network service posts published between January 1 and December 31, 2019 were analyzed. The procedures in this study were conducted in the following order: keyword selection, data collection, morpheme analysis, keyword analysis, and topic modeling. Results: The term "yirundung-yi", which is a native Korean word referring to premature infants, was confirmed to be a useful term for parents. The following four topics were identified as the supportive care needs of parents of preterm children: 1) a vague fear of caring for a baby upon imminent neonatal intensive care unit discharge, 2) real-world difficulties encountered while caring for preterm children, 3) concerns about growth and development problems, and 4) anxiety about possible complications. Conclusion: Supportive care interventions for parents of preterm children should include general parenting methods for babies. A team composed of multidisciplinary experts must support the individual growth and development of preterm children and manage the complications of prematurity using highly accessible media.

Purpose: This study aimed to identify the main keywords, network structures, and topical themes in patient safety incident reports using text network analysis. Methods: The study analyzed patient safety incident reports from a general hospital in Seoul, covering a total of 3,576 cases reported over five years, from 2019 to 2023. Unstructured data were extracted from the text of the incident reports, detailing how the patient safety incidents occurred and how they were managed according to the six-part principles. The analysis was conducted in four steps: 1) word extraction and refinement, 2) keyword extraction and word network generation, 3) network connectivity and centrality analysis, and 4) topic modeling analysis. The NetMiner program was used for data analysis. Results: The analysis of degree, betweenness, and closeness centrality revealed that the most common keywords among the top five were "confirmation," "medication," "inpatient room," "caregiver," and "condition." Topic modeling analysis identified three main topic groups: 1) incidents caused by a lack of awareness of fall risk, 2) incidents of non-compliance with basic medication principles, and 3) incidents due to inaccurate patient identification. Conclusion: To prevent patient safety incidents, it is necessary to promote a culture of safety in hospitals, standardize patient identification procedures, and provide basic training in medication safety and fall prevention to healthcare staff. Furthermore, empirical research on patient safety practices is necessary to encourage active participation in patient safety activities by patients and family caregivers.

본 논문에서는 주성분 회귀법과 부분최소자승 회귀법을 비교하여 보여준다. 이 비교의 목적은 선형형태를 보유한 근적외선 분광 데이터의 분석에 사용할 수 있는 적합한 예측 방법을 찾기 위해서이다. 두 가지 데이터 마이닝 방법 론인 주성분 회귀법과 부분최소자승 회귀법이 비교되어 질 것이다. 본 논문에서는 부분최소자승 회귀법은 주성분 회귀법과 비교했을 때 약간 나은 예측능력을 가진 결과를 보여준다. 주성분 회귀법에서 50개의 주성분이 모델을 생 성하기 위해서 사용지만 부분최소자승 회귀법에서는 12개의 잠재요소가 사용되었다. 평균제곱오차가 예측능력을 측 정하는 도구로 사용되었다. 본 논문의 근적외선 분광데이터 분석에 따르면 부분최소자승회귀법이 선형경향을 가진 데이터의 예측에 가장 적합한 모델로 판명되었다.

Data mining techniques develop a more accurate classification algorithm for patients classified as either normotensive, prehypertensive, or hypertensive. Logistic Model Tree, NBTree, and Bagging were chosen as the three classification models with tenfold cross-validation (LMT). Over 24 hours, we collected ABP readings from 1161 patients. To analyze the data, data mining techniques were used and a tool called WEKA. The data was analyzed based on age, gender, wake-up blood pressure, medication, sleep-up blood pressure, and overall blood pressure. According to bagging results, 886 cases (76.3 percent) are correctly classified, with 270 cases classified as pre-hypertensive, 436 cases as Normotensive, and 180 cases as hypertensive. NBTree's results show that 882 (75.9%) of the 1161 instances are correctly classified. Pre-hypertensive patients make up 256, normotensive patients 442, and hypertensive patients 184. Of the 1161 instances, the LMT algorithm correctly classified 878 (75.6 percent). According to the results, 275 people are pre-hypertensive, 431 are normotensive, and 172 are hypertensive. According to our findings, bagging is the most accurate classifier for the 24 hour ABP Monitoring dataset we used. Bagging achieves less overfitting because it focuses on global accuracy. It stabilizes and improves the accuracy of unstable methods compared to single classifiers.

‘디지털 트윈(Digital Twin)’은 4차산업혁명의 주요 기술들로 유통물류 산업에서 크게 각광받고 있는 기술이다. 현실에 적용하기 전에 가상 환경 공간에서 시뮬레이션을 통해 결과를 예측할 수 있기에 시간이나 비용적인 측면에서 큰 도움을 받을 수 있다. 하지만 디지털 트윈은 아직까지 획일화된 정립이나 사람마다의 기술의 이해도가 다소 부족한 부분이 있다. 이러한 디지털 트윈 기술의 이해와 활용도를 제고하고자 트렌드 분석 연구 방법을 제안하고자 한다. 본 연구는 디지털 트윈에 대한 대중들과 언론의 인식이 어떻게 변화하였는지를 파악하기 위하여, 시기별로 디지털 트윈에 대한 인식의 흐름을 뉴스 데이터를 기반으로 확인하고, 전처리 된 데이터를 시각화(Pandas, Tableau)하여 변화에 대한 데이터의 흐름을 파악하고자 한다. 본 연구에서는 데이터를 분석하기 위해서 파이썬 라이브러리인 Selenium을 통해 웹 크롤링을 하여 데이터를 수집하였고, 수집된 데이터에 대한 칼럼의 프로세싱 과정 이후 불용어 처리를 위해 불용어 사전을 정의하고 정의된 불용어 사전을 통해 데이터 훈련할 때마다 생겨나는 불용어들을 사전에 저장하고 계속해서 데이터를 정제한다. 이후 머신러닝을 통해 분류 작업을 거쳐 결과물을 도출하고, 이후 파이썬 라이브러리인 Pandas와 시각화 전문 툴인 Tableau를 활용하여 시각화 및 통계 작업을 수행한다. 도출된 결과를 바탕으로 다각도의 디지털 트윈 연구 방향성을 대한 시사점을 제시하고자 한다. 본 연구는 뉴스 데이터 웹 크롤링과 데이터 분석 및 불용어 전처리를 통하여 디지털 트윈 기술의 동향과 언론의 인식을 효과적으로 분석할 수 있다는데 학술적 의의가 있다.

본 논문에서는 의사결정(Decision Making)의 두 가지 접근 방식인 분석적 의사결정 방식(Analytical Decision Making Approach)과 직관적 의사결정 방식(Intuitive Decision Making Approach)의 비교를 위한 방법과 절차를 제시한다. 기존 연구에서는 전문성이 담보된 상황에서의 직관적 의사결정 방식의 상대적 효율성이 입증되어온 흐름이었으나 최근의 빅데이터 기술의 발전은 분석적 의사결정 방식을 부상시키고 있다. 이러한 배경에서 본 연구는 분석적 의사결정 방식과 직관적 의사결정 방식의 비교를 위해 빅데이터 분석의 대표적 사례인 고객이탈 분석 맥락에서 수행한다. 구체적으로는 신협중앙회의 체크카드 상품 A를 대상으로 하며 분석적 의사결정 지원도구로서는 데이터 마이닝(Data Mining) 기법을 활용하고, 직관적 의사결정을 측정하는 도구로서 개인의 선호와 인지를 분석할 수 있는 계층화 분석 과정(Analytical Hierarchy Process: 이하 AHP)를 사용한다. 이에 따라 고객이탈 원인 분석 문제에 있어서 서로 다른 접근방식의 분석 결과를 비교하기 위한 방법론을 제시하고자 한다.