[논문]우수 의약품 제조 기준 위반 패턴 인식을 위한 연관규칙과 텍스트 마이닝 기반 t-SNE분석

이준오; 손소영

doi:10.7469/jksqm.2022.50.4.717

우수 의약품 제조 기준 위반 패턴 인식을 위한 연관규칙과 텍스트 마이닝 기반 t-SNE분석
Violation Pattern Analysis for Good Manufacturing Practice for Medicine using t-SNE Based on Association Rule and Text Mining 원문보기

品質經營學會誌 = Journal of Korean society for quality management, v.50 no.4, 2022년, pp.717 - 734

Abstract ▼ AI-Helper

Purpose: The purpose of this study is to effectively detect violations that occur simultaneously against Good Manufacturing Practice, which were concealed by drug manufacturers. Methods: In this study, we present an analysis framework for analyzing regulatory violation patterns using Association Rule Mining (ARM), Text Mining, and t-distributed Stochastic Neighbor Embedding (t-SNE) to increase the effectiveness of on-site inspection. Results: A number of simultaneous violation patterns was discovered by applying Association Rule Mining to FDA's inspection data collected from October 2008 to February 2022. Among them there were 'concurrent violation patterns' derived from similar regulatory ranges of two or more regulations. These patterns do not help to predict violations that simultaneously appear but belong to different regulations. Those unnecessary patterns were excluded by applying t-SNE based on text-mining. Conclusion: Our proposed approach enables the recognition of simultaneous violation patterns during the on-site inspection. It is expected to decrease the detection time by increasing the likelihood of finding intentionally concealed violations.

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표/그림 (14)

표 Table 1. 21 CFR 211 Regulatory content by subpart
표 Table 2. Type of data used for on-site inspection
그림 Figure 1. Pareto Chart of GMP Regulatory Violations
표 Table 3. Top 5 violations observed by individual regulatory units and their percentages
표 Table 4. Frequency and share of violations observed on a subpart basis
표 Table 5. Regulatory violations rules derived from Subpart regulations
표 Table 6. Association rules between subpart, sub-regulations
표 Table 7. Frequency Value of Top 10 words
표 Table 8. 28 regulations with similar scope of discipline
그림 Figure 2. Dimension reduction visualization of vectorized regulation using t-SNE, PCA, LLE, MDS
표 Table 9. Parameter setting values for the experiments
그림 Figure 3. Network graph for analysis of violation patterns in sub-regulation between Subpart G 'Packaging and Labeling Control' and Subpart J 'Records and Reports'
그림 Figure 4. Contour plot showing the standardized values of distance between nodes in a network graph for analysis of sub-regulation violation patterns between Subpart G 'Packaging and Labeling Control' and Subpart J 'Recordings and Reports'
표 Table 10. Content of regulations [21 CFR 211.122(d), 21 CFR 211.188(b)(8), 21 CFR211.192] and location characteristics between two nodes shown in Figure 3, 4

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