Document Detail


Computer based extraction of phenoptypic features of human congenital anomalies from the digital literature with natural language processing techniques.
MedLine Citation:
PMID:  25160250     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
The lack of laboratory tests for the diagnosis of most of the congenital anomalies renders the physical examination of the case crucial for the diagnosis of the anomaly; and the cases in the diagnostic phase are mostly being evaluated in the light of the literature knowledge. In this respect, for accurate diagnosis, ,it is of great importance to provide the decision maker with decision support by presenting the literature knowledge about a particular case. Here, we demonstrated a methodology for automated scanning and determining of the phenotypic features from the case reports related to congenital anomalies in the literature with text and natural language processing methods, and we created a framework of an information source for a potential diagnostic decision support system for congenital anomalies.
Authors:
Gökhan Karakülah; Oğuz Dicle; Ozgün Koşaner; Aslı Suner; Cağdaş Can Birant; Tolga Berber; Sezin Canbek
Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Studies in health technology and informatics     Volume:  205     ISSN:  0926-9630     ISO Abbreviation:  Stud Health Technol Inform     Publication Date:  2014  
Date Detail:
Created Date:  2014-08-27     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9214582     Medline TA:  Stud Health Technol Inform     Country:  Netherlands    
Other Details:
Languages:  eng     Pagination:  570-4     Citation Subset:  T    
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