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一种基于规则的出院小结中连续护理识别方法。

A rule-based method for continuity of care identification in discharge summaries.

作者信息

Silva e Oliveira Lucas E, de Souza Andréia C, Nohama Percy, Moro Claudia M C

机构信息

Health Technology Post-Graduate Program, Polytechnic School, Pontifical Catholic University of Paraná, Brazil.

出版信息

Stud Health Technol Inform. 2013;192:1221.

Abstract

Discharge summaries are an important clinical narrative as they include the continuity of care information. Identification of data contained in their text is a difficult task due to its freeform text and lack of consensus on essential content. This research proposes a rule-based method to verify the presence of information about continuity of care in Portuguese texts, applying Natural Language Processing (NLP) techniques, and based on an annotated medical corpus. After the experiments, 4 rules were defined and applied in the text of 200 summaries to identify if they have or not the continuity of care information. This process had resulted in Precision value of 84%, Recall value of 70%, Specificity value of 97% and F-Measure value of 76% related to algorithm evaluation.

摘要

出院小结是一份重要的临床记录,因为它们包含了连续护理信息。由于其文本形式自由且对基本内容缺乏共识,识别其中包含的数据是一项艰巨的任务。本研究提出了一种基于规则的方法,运用自然语言处理(NLP)技术,并基于一个带注释的医学语料库,来验证葡萄牙语文本中是否存在连续护理信息。实验后,定义了4条规则并应用于200份小结的文本中,以确定它们是否具有连续护理信息。该过程在算法评估方面的精确率为84%,召回率为70%,特异性为97%,F值为76%。

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