Detecting Noun Phrases in Biomedical Terminologies: The First Step in Managing the Evolution of Knowledge
In order to identify variations between two or several versions of Clinical Practice Guidelines,we propose a method based on the detection of noun phrases.Currently,we are developing a comparison approach to extract similar and different elements between medical documents in French in order to identify any significant changes such as new medical terms or concepts,new treatments etc.In this paper,we describe a basic initial step for this comparison approach i.e.detecting noun phrases.This step is based on patterns constructed from six main medical terminologies used in document indexing.The patterns are constructed by using a Tree Tagger.To avoid a great number of generated patterns,the most relevant ones are selected that are able identify more than 80% of the six terminologies used in this study.These steps allowed us to obtain a manageable list of 262 patterns which have been evaluated.Using this list of patterns,708 maximal noun phrases were found,with,364 correct phrases which represent a 51.41% precision.However by detecting these phrases manually,602 maximal noun phrases were found which represent a 60.47% recall and therefore a 55.57% F-measure.We attempted to improve these results by increasing the number of patterns from 262 to 493.A total of 729 maximal noun phrases were obtained,with 365 which were correct,and corresponded to a 50.07% precision,60.63% recall and 54.85% F-measure.
Biomedical terminologies medical knowledge evolution Natural Language Processing Clinical Practice Guidelines noun phrases detection patterns
Adila Merabti Lina F. Soualmia Stéfan J. Darmoni
CISMeF, TIBS LITIS Laboratory EA 4108, Rouen University Hospital, France CISMeF, TIBS LITIS Laboratory EA 4108, Rouen University Hospital, France;LIMICS, French National Ins
国际会议
The Third International Coference on Health Information Science(HIS2014)2014年第三届健康信息学国际学术会议
深圳
英文
109-120
2014-04-22（万方平台首次上网日期，不代表论文的发表时间）