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Using Natural Language Processing to Enable In-Depth Analysis of Clinical Messages Posted to an Internet Mailing List

A Feasibility Study

We developed a workflow for finding a manageable number of clinically relevant messages from a much larger corpus of messages posted to an Internet mailing list. Our procedures enable labor-intensive content analyses by retrieving a set of messages tailored to the research question of a qualitative research team.

Type of project: academic


Involved Persons:

Project Leader: Tanja Bekhuis (University of Pittsburgh School of Medicine)
Other involved People: Marcos Kreinacke, Heiko Spallek, Mei Song, Jean O'Donnell
heiko spallek (89 star_green)
jean o'donnell (3 star_blue)
mei song (4 star_blue)
Bakhtinur Khudanov (5 star_blue)
Marcos Kreinacke (10 star_purple)
Beenish Chaudry ( star_blue)


Is the project funded?yes
Funding source:Partially funded by the Pittsburgh Biomedical Informatics Training Program 5T15LM007059

Other information:

Papers related to project:Bekhuis, T., Kreinacke M., Spallek H., Song M., O’Donnell J. (in press). Using Natural Language Processing to Enable In-depth Analysis of Clinical Messages Posted to an Internet Mailing List: A Feasibility Study. Journal of Medical Internet Research. doi:10.2196/jmir.1799. Bekhuis T., Kreinacke M., Spallek H., Song M. (2010). Using the Natural Language Toolkit to reduce the number of messages for in-depth content analyses: A case study. AMIA 2010 Annu Symp Proc, 981. Washington, DC: American Medical Informatics Association.

DIOC user:

tbekhuis (9 star_purple), University of Pittsburgh
tbekhuis's interests: evidence-based dentistry/electronic information resources, research methods, library information sciences, medical research informatics, NLP, machine learning, ontologies


J E Richardson, DMD, MET (3 ), 07/18/2011:
Can you elaborate on your research question?
Bakhtinur Khudanov (5 ), 07/14/2011:
Is this what you gather information from patients, analyze it and diagnose and make treatment plan?


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