Literature DB >> 24551335

Semantic processing to identify adverse drug event information from black box warnings.

Adam Culbertson1, Marcelo Fiszman1, Dongwook Shin1, Thomas C Rindflesch1.   

Abstract

We utilized a semantic natural language processing approach to extract adverse drug event information from FDA black box warnings. Overall performance was 90% precision, 51% recall, and 0.65 F-Score. Information extracted can be stored in a structured format and may be useful to support clinical decision support systems.

Mesh：

Year:  2013        PMID： 24551335      PMCID： PMC3900176     

Source DB:  PubMed          Journal:  AMIA Annu Symp Proc        ISSN： 1559-4076


  3 in total

1.  The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text.

Authors:  Thomas C Rindflesch; Marcelo Fiszman
Journal:  J Biomed Inform       Date:  2003-12       Impact factor: 6.317

2.  ADESSA: A Real-Time Decision Support Service for Delivery of Semantically Coded Adverse Drug Event Data.

Authors:  Jon D Duke; Jeff Friedlin
Journal:  AMIA Annu Symp Proc       Date:  2010-11-13

3.  Mining FDA drug labels using an unsupervised learning technique--topic modeling.

Authors:  Halil Bisgin; Zhichao Liu; Hong Fang; Xiaowei Xu; Weida Tong
Journal:  BMC Bioinformatics       Date:  2011-10-18       Impact factor: 3.169
  3 in total
  2 in total

1.  Semantic processing to identify adverse drug event information from black box warnings.

Authors:  Adam Culbertson; Marcelo Fiszman; Dongwook Shin; Thomas C Rindflesch
Journal:  AMIA Annu Symp Proc       Date:  2014-11-14

2.  Automatic Classification of Structured Product Labels for Pregnancy Risk Drug Categories, a Machine Learning Approach.

Authors:  Laritza M Rodriguez; Dina Demner Fushman
Journal:  AMIA Annu Symp Proc       Date:  2015-11-05
  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.