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Literature DB >> 16779117

Medical facts to support inferencing in natural language processing.

Thomas C Rindflesch¹, Serguei V Pakhomov, Marcelo Fiszman, Halil Kilicoglu, Vincent R Sanchez.

Abstract

We report on the use of medical facts to support the enhancement of natural language processing of biomedical text. Inferencing in semantic interpretation depends on a fact repository as well as an ontology. We used statistical methods to construct a repository of drug-disorder co-occurrences from a large collection of clinical notes, and this resource is used to validate inferences automatically drawn during semantic interpretation of Medline citations about pharmacologic interventions for disease. We evaluated the results against a published reference standard for treatment of diseases.

Entities: Disease

Mesh：

Year: 2005 PMID： 16779117 PMCID： PMC1560850

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

9 in total

1. Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program.

Authors: A R Aronson
Journal: Proc AMIA Symp Date: 2001

2. Maximum entropy modeling for mining patient medication status from free text.

Authors: Serguei V Pakhomov; Alexander Ruggieri; Christopher G Chute
Journal: Proc AMIA Symp Date: 2002

3. 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

4. The UMLS knowledge source server: an object model for delivering UMLS data.

Authors: Anantha Bangalore; Karen E Thorn; Carolyn Tilley; Lee Peters
Journal: AMIA Annu Symp Proc Date: 2003

5. A simple algorithm for identifying negated findings and diseases in discharge summaries.

Authors: W W Chapman; W Bridewell; P Hanbury; G F Cooper; B G Buchanan
Journal: J Biomed Inform Date: 2001-10 Impact factor: 6.317

6. Automated encoding of clinical documents based on natural language processing.

Authors: Carol Friedman; Lyudmila Shagina; Yves Lussier; George Hripcsak
Journal: J Am Med Inform Assoc Date: 2004-06-07 Impact factor: 4.497

7. Developing optimal search strategies for detecting clinically relevant qualitative studies in MEDLINE.

Authors: Sharon S-L Wong; Nancy L Wilczynski; R Brian Haynes
Journal: Stud Health Technol Inform Date: 2004

8. MedPost: a part-of-speech tagger for bioMedical text.

Authors: L Smith; T Rindflesch; W J Wilbur
Journal: Bioinformatics Date: 2004-04-08 Impact factor: 6.937

9. Lexical methods for managing variation in biomedical terminologies.

Authors: A T McCray; S Srinivasan; A C Browne
Journal: Proc Annu Symp Comput Appl Med Care Date: 1994

9 in total

13 in total

1. Detection of practice pattern trends through Natural Language Processing of clinical narratives and biomedical literature.

Authors: Elizabeth S Chen; Peter D Stetson; Yves A Lussier; Marianthi Markatou; George Hripcsak; Carol Friedman
Journal: AMIA Annu Symp Proc Date: 2007-10-11

Medical facts to support inferencing in natural language processing.

1. Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program.

2. Maximum entropy modeling for mining patient medication status from free text.

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

4. The UMLS knowledge source server: an object model for delivering UMLS data.

5. A simple algorithm for identifying negated findings and diseases in discharge summaries.

6. Automated encoding of clinical documents based on natural language processing.

7. Developing optimal search strategies for detecting clinically relevant qualitative studies in MEDLINE.

8. MedPost: a part-of-speech tagger for bioMedical text.

9. Lexical methods for managing variation in biomedical terminologies.

1. Detection of practice pattern trends through Natural Language Processing of clinical narratives and biomedical literature.

2. Learning to identify treatment relations in clinical text.

3. Assessing the role of a medication-indication resource in the treatment relation extraction from clinical text.

4. Using SemRep to label semantic relations extracted from clinical text.

5. Extracting drug indication information from structured product labels using natural language processing.

6. Selecting information in electronic health records for knowledge acquisition.

7. Towards a characterization of apparent contradictions in the biomedical literature using context analysis.

8. Determining the reasons for medication prescriptions in the EHR using knowledge and natural language processing.

9. A textual representation scheme for identifying clinical relationships in patient records.

10. Automated acquisition of disease drug knowledge from biomedical and clinical documents: an initial study.