Linguistic approach for identification of medication names and related information in clinical narratives
Linguistic approach for identification of medication names and related information in clinical narratives
复制标题
DOI：
10.1136/jamia.2010.004036
复制
发表时间：
2010-09-01
影响因子：
6.4
通讯作者：
Grabar, Natalia
中科院分区：
文献类型：
--
作者：
Hamon, Thierry;Grabar, Natalia
药物治疗是任何医疗过程中不可或缺的一部分，在大多数患者的病史中起着重要作用。药物信息对于药物警戒、医疗决策或生物医学研究等任务至关重要。在叙事文本中，与药物相关的信息可能隐藏在其他不相关的数据中。必须设计特定的方法，例如文本挖掘提供的方法来访问它们，这是本研究的目的。方法设计临床叙事性文献分析系统，提取临床叙事性文献中的用药情况和用药相关信息。该系统还试图推断出所使用的字典中没有涵盖的药物。该系统提供的结果在2009年举行的1282 NLP挑战框架内进行了评估。该系统的f值为0.78，在20个参赛队中排名第7位（最高f值为0.86）。系统对药物名称、用药频次、给药剂量和给药方式的标注和提取效果较好（F-measure > 0.81），但对持续时间和原因信息的标注和提取效果较差（F-measure分别为0.36和0.29）。在训练集和测试集之间，系统的性能是稳定的。
Background Pharmacotherapy is an integral part of any medical care process and plays an important role in the medical history of most patients. Information on medication is crucial for several tasks such as pharmacovigilance, medical decision or biomedical research.Objectives Within a narrative text, medication-related information can be buried within other non-relevant data. Specific methods, such as those provided by text mining, must be designed for accessing them, and this is the objective of this study.Methods The authors designed a system for analyzing narrative clinical documents to extract from them medication occurrences and medication-related information. The system also attempts to deduce medications not covered by the dictionaries used.Results Results provided by the system were evaluated within the framework of the 1282 NLP challenge held in 2009. The system achieved an F-measure of 0.78 and ranked 7th out of 20 participating teams (the highest F-measure was 0.86). The system provided good results for the annotation and extraction of medication names, their frequency, dosage and mode of administration (F-measure over 0.81), while information on duration and reasons is poorly annotated and extracted (F-measure 0.36 and 0.29, respectively). The performance of the system was stable between the training and test sets.