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Automated systems to identify relevant documents in product risk management

机译：自动化系统以识别产品风险管理中的相关文件

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BackgroundProduct risk management involves critical assessment of the risks and benefits of health products circulating in the market. One of the important sources of safety information is the primary literature, especially for newer products which regulatory authorities have relatively little experience with. Although the primary literature provides vast and diverse information, only a small proportion of which is useful for product risk assessment work. Hence, the aim of this study is to explore the possibility of using text mining to automate the identification of useful articles, which will reduce the time taken for literature search and hence improving work efficiency. In this study, term-frequency inverse document-frequency values were computed for predictors extracted from the titles and abstracts of articles related to three tumour necrosis factors-alpha blockers. A general automated system was developed using only general predictors and was tested for its generalizability using articles related to four other drug classes. Several specific automated systems were developed using both general and specific predictors and training sets of different sizes in order to determine the minimum number of articles required for developing such systems.
机译：背景产品风险管理涉及对市场上流通的保健产品的风险和收益进行严格评估。安全信息的重要来源之一是主要文献，尤其是对于监管机构经验不足的较新产品。尽管主要文献提供了广泛而多样的信息，但其中只有一小部分对产品风险评估工作有用。因此，本研究的目的是探索使用文本挖掘自动识别有用文章的可能性，这将减少文献检索所花费的时间，从而提高工作效率。在这项研究中，计算了从与三个肿瘤坏死因子-α阻滞剂有关的文章的标题和摘要中提取的预测变量的词频逆文档频率值。仅使用通用预测变量开发了通用自动化系统，并使用与其他四种药物类别相关的文章测试了通用性。为了确定开发此类系统所需的最少文章数量，使用了通用预测器和特定预测器以及不同大小的训练集开发了几种特定的自动化系统。

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