Mirzapour Mehdi, Abdaoui Amine, Tchechmedjiev Andon, Digan William, Bringay Sandra, Jonquet Clement
Laboratory of Informatics, Robotics and Microelectronics of Montpellier (LIRMM), University of Montpellier, CNRS, France.
Laboratory of Informatics, Robotics and Microelectronics of Montpellier (LIRMM), University of Montpellier, CNRS, France; EuroMov Digital Health in Motion, Univ Montpellier, IMT Mines Alès, France.
J Biomed Inform. 2021 May;117:103733. doi: 10.1016/j.jbi.2021.103733. Epub 2021 Mar 15.
The context of medical conditions is an important feature to consider when processing clinical narratives. NegEx and its extension ConText became the most well-known rule-based systems that allow determining whether a medical condition is negated, historical or experienced by someone other than the patient in English clinical text. In this paper, we present a French adaptation and enrichment of FastContext which is the most recent, n-trie engine-based implementation of the ConText algorithm. We compiled an extensive list of French lexical cues by automatic and manual translation and enrichment. To evaluate French FastContext, we manually annotated the context of medical conditions present in two types of clinical narratives: (i)death certificates and (ii)electronic health records. Results show good performance across different context values on both types of clinical notes (on average 0.93 and 0.86 F1, respectively). Furthermore, French FastContext outperforms previously reported French systems for negation detection when compared on the same datasets and it is the first implementation of contextual temporality and experiencer identification reported for French. Finally, French FastContext has been implemented within the SIFR Annotator: a publicly accessible Web service to annotate French biomedical text data (http://bioportal.lirmm.fr/annotator). To our knowledge, this is the first implementation of a Web-based ConText-like system in a publicly accessible platform allowing non-natural-language-processing experts to both annotate and contextualize medical conditions in clinical notes.
在处理临床叙述时，医疗状况的背景是一个需要考虑的重要特征。NegEx及其扩展ConText成为了最著名的基于规则的系统，可用于确定英文临床文本中医疗状况是否被否定、是否为历史状况或是否由患者以外的其他人经历过。在本文中，我们展示了对FastContext的法语改编和扩充，FastContext是ConText算法的最新基于n-trie引擎的实现。我们通过自动和手动翻译及扩充编译了一份广泛的法语词汇线索列表。为了评估法语FastContext，我们手动标注了两种临床叙述中存在的医疗状况背景：（i）死亡证明和（ii）电子健康记录。结果表明，在这两种临床记录上，不同背景值的表现都很好（平均F1值分别为0.93和0.86）。此外，在相同数据集上进行比较时，法语FastContext在否定检测方面优于先前报道的法语系统，并且它是首次针对法语报道的上下文时间性和经历者识别的实现。最后，法语FastContext已在SIFR注释器中实现：这是一个可公开访问的网络服务，用于注释法语文本生物医学数据（http://bioportal.lirmm.fr/annotator）。据我们所知，这是在可公开访问的平台上首次实现的类似基于网络的ConText系统，允许非自然语言处理专家对临床记录中的医疗状况进行注释和上下文标注。
