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염윤진,김동재,Yum, Yunjin,Kim, Dongjae 한국통계학회 2018 응용통계연구 Vol.31 No.4
결측치를 대치하는 여러가지 단일대치법 중에서 다변량 정규성 등의 모수적 모형이 만족되지 않을 때에도 강건성(robustness)을 지니는 k-최근접 이웃 대치법(k-nearest neighbors; KNN)이 널리 활용된다. KNN대치법에서 자료의 국소적 특징을 반영한 적응 최근접 이웃(adaptive nearest neighbors; ANN) 대치법과 k개의 최근접 이웃들 중 극단값이나 이상값이 있는 경우 이들의 영향에 덜 민감한 가중 k-최근접 이웃(weighted KNN; WKNN) 대치법의 장점을 결합한 가중 적응 최근접 이웃(weighted ANN; WANN) 대치법을 제안하였다. 또한 모의실험을 통하여 기존의 방법들과 제안한 방법을 비교하였다. Widely used among the various single imputation methods is k-nearest neighbors (KNN) imputation due to its robustness even when a parametric model such as multivariate normality is not satisfied. We propose a weighted adaptive nearest neighbors imputation method that combines the adaptive nearest neighbors imputation method that accounts for the local features of the data in the KNN imputation method and weighted k-nearest neighbors method that are less sensitive to extreme value or outlier among k-nearest neighbors. We conducted a Monte Carlo simulation study to compare the performance of the proposed imputation method with previous imputation methods.
유학제,염윤진,박수완,이정문,장문정,김유중,김종호,박현준,박재형,주형준 대한의료정보학회 2023 Healthcare Informatics Research Vol.29 No.2
Objectives: Electrocardiography (ECG)-based diagnosis by experts cannot maintain uniform quality because individual differencesmay occur. Previous public databases can be used for clinical studies, but there is no common standard that wouldallow databases to be combined. For this reason, it is difficult to conduct research that derives results by combining databases. Recent commercial ECG machines offer diagnoses similar to those of a physician. Therefore, the purpose of this study was toconstruct a standardized ECG database using computerized diagnoses. Methods: The constructed database was standardizedusing Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) and Observational Medical Outcomes Partnership–common data model (OMOP-CDM), and data were then categorized into 10 groups based on the Minnesota classification. In addition, to extract high-quality waveforms, poor-quality ECGs were removed, and database bias was minimizedby extracting at least 2,000 cases for each group. To check database quality, the difference in baseline displacement accordingto whether poor ECGs were removed was analyzed, and the usefulness of the database was verified with seven classificationmodels using waveforms. Results: The standardized KURIAS-ECG database consists of high-quality ECGs from 13,862 patients,with about 20,000 data points, making it possible to obtain more than 2,000 for each Minnesota classification. An artificialintelligence classification model using the data extracted through SNOMED-CT showed an average accuracy of 88.03%. Conclusions: The KURIAS-ECG database contains standardized ECG data extracted from various machines. The proposedprotocol should promote cardiovascular disease research using big data and artificial intelligence.