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http://dx.doi.org/10.36498/kbigdt.2021.6.2.61

Statistical Data Extraction and Validation from Graph for Data Integration and Meta-analysis  

Sung Ryul Shim (차의과학대학교 의학전문대학원 정보의학교실)
Yo Hwan Lim (차의과학대학교 의학전문대학원 정보의학연구소)
Myunghee Hong (차의과학대학교 의학전문대학원 정보의학교실)
Gyuseon Song (차의과학대학교 의학전문대학원 정보의학교실)
Hyun Wook Han (차의과학대학교 의학전문대학원 정보의학교실)
Publication Information
The Journal of Bigdata / v.6, no.2, 2021 , pp. 61-70 More about this Journal
Abstract
The objective of this study was to describe specific approaches for data extraction from graph when statistical information is not directly reported in some articles, enabling data intergration and meta-analysis for quantitative data synthesis. Particularly, meta-analysis is an important analysis tool that allows the right decision making for evidence-based medicine by systematically and objectively selects target literature, quantifies the results of individual studies, and provides the overall effect size. For data integration and meta-analysis, we investigated the strength points about the introduction and application of Adobe Acrobet Reader and Python-based Jupiter Lab software, a computer tool that extracts accurate statistical figures from graphs. We used as an example data that was statistically verified throught an previous studies and the original data could be obtained from ClinicalTrials.gov. As a result of meta-analysis of the original data and the extraction values of each computer software, there was no statistically significant difference between the extraction methods. In addition, the intra-rater reliability of between researchers was confirmed and the consistency was high. Therefore, In terms of maintaining the integrity of statistical information, measurement using a computational tool is recommended rather than the classically used methods.
Keywords
Evidence based medicine; Meta-analysis; Systematic review; Research synthesize; Computer graphics; Measurement program; Data extraction;
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