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首页> 外文期刊>Radiotherapy and oncology: Journal of the European Society for Therapeutic Radiology and Oncology >Benefits of a clinical data warehouse with data mining tools to collect data for a radiotherapy trial
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Benefits of a clinical data warehouse with data mining tools to collect data for a radiotherapy trial

机译：具有数据挖掘工具的临床数据仓库的好处是可以收集放射治疗试验的数据

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Introduction: Collecting trial data in a medical environment is at present mostly performed manually and therefore time-consuming, prone to errors and often incomplete with the complex data considered. Faster and more accurate methods are needed to improve the data quality and to shorten data collection times where information is often scattered over multiple data sources. The purpose of this study is to investigate the possible benefit of modern data warehouse technology in the radiation oncology field. Material and methods: In this study, a Computer Aided Theragnostics (CAT) data warehouse combined with automated tools for feature extraction was benchmarked against the regular manual data-collection processes. Two sets of clinical parameters were compiled for non-small cell lung cancer (NSCLC) and rectal cancer, using 27 patients per disease. Data collection times and inconsistencies were compared between the manual and the automated extraction method. Results: The average time per case to collect the NSCLC data manually was 10.4 ± 2.1 min and 4.3 ± 1.1 min when using the automated method (p < 0.001). For rectal cancer, these times were 13.5 ± 4.1 and 6.8 ± 2.4 min, respectively (p < 0.001). In 3.2% of the data collected for NSCLC and 5.3% for rectal cancer, there was a discrepancy between the manual and automated method. Conclusions: Aggregating multiple data sources in a data warehouse combined with tools for extraction of relevant parameters is beneficial for data collection times and offers the ability to improve data quality. The initial investments in digitizing the data are expected to be compensated due to the flexibility of the data analysis. Furthermore, successive investigations can easily select trial candidates and extract new parameters from the existing databases.
机译：简介：目前，在医疗环境中收集试验数据大部分是手动执行的，因此非常耗时，容易出错，并且通常不会考虑所考虑的复杂数据。需要更快，更准确的方法来改善数据质量并缩短数据收集时间，因为信息通常分散在多个数据源上。这项研究的目的是调查现代数据仓库技术在放射肿瘤学领域的可能利益。材料和方法：在这项研究中，将计算机辅助Theragnostics（CAT）数据仓库与用于特征提取的自动化工具相结合，以常规的手动数据收集过程为基准。针对每种疾病使用27名患者，针对非小细胞肺癌（NSCLC）和直肠癌编制了两组临床参数。比较了手动和自动提取方法之间的数据收集时间和不一致之处。结果：使用自动方法时，每例手动收集NSCLC数据的平均时间为10.4±2.1分钟和4.3±1.1分钟（p <0.001）。对于直肠癌，这些时间分别为13.5±4.1分钟和6.8±2.4分钟（p <0.001）。在非小细胞肺癌的3.2％数据和直肠癌的5.3％数据中，手动和自动方法之间存在差异。结论：将一个数据仓库中的多个数据源与用于提取相关参数的工具结合起来，对于数据收集时间是有益的，并且可以提高数据质量。由于数据分析的灵活性，预计将对数字化数据进行的最初投资得到补偿。此外，连续的调查可以轻松地选择候选试验并从现有数据库中提取新参数。

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