Paper Info
Title
Extracting clinical information from free-text of pathology and operation notes via Chinese natural language processing
Abstract
Many of surgical records containing the clinical information are in electronic forms, but a lot of them are still in free-text format in China. In this paper, we have an attempt to extract information with the Nature Language Processing (NLP) approach. The procedure of NLP is made up of three steps. First, given 36 free-text of operation notes, a physician manually annotates the information which he is interested in. Second, we extract the features of the annotated information. Third, several logistic regression models are built. Totally, 14 clinical data are extracted. The NLP tool was tested 364 operation notes. The accuracy of extraction is between 67.3%-96.7%. Our results indicate that the performance of the features we used to build the machine learning is good in extracting useful information from free-text Chinese operation notes for liver cancer. In the future, these features would explored on more broader clinical settings.
Year
DOI
Venue
2010
10.1109/BIBMW.2010.5703867
Bioinformatics and Biomedicine Workshops
Keywords
DocType
Volume
operation note,medical information systems,nature language processing (nlp),chinese nature language processing (nlp),electronic medical record (emr),surgical record,free text format,clinical information extraction,natural language processing,nature language processing approach,chinese natural language processing,machine learning,bioinformatics,pathology note,feature extraction,solid modeling,surgery,logistic regression model,data mining,cancer
Conference
null
Issue
ISSN
ISBN
null
null
978-1-4244-8304-4
Citations 
PageRank 
References 
2
0.39
4
Authors
5
Name
Order
Citations
PageRank
Qiang Zeng112612.91
Xiaoyan Zhang220.73
Zuofeng Li3587.76
Lei Liu4674.86
Weide Zhang5151.09