Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

Using natural language processing to enable in-depth analysis of clinical messages posted to an internet mailing list: a feasibility study

Bekhuis, T and Kreinacke, M and Spallek, H and Song, M and O'Donnell, JA (2011) 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, 13 (4).

Published Version

Download (134kB) | Preview
[img] Plain Text (licence)
Download (1kB)


Background: An Internet mailing list may be characterized as a virtual community of practice that serves as an information hub with easy access to expert advice and opportunities for social networking. We are interested in mining messages posted to a list for dental practitioners to identify clinical topics. Once we understand the topical domain, we can study dentists’ real information needs and the nature of their shared expertise, and can avoid delivering useless content at the point of care in future informatics applications. However, a necessary first step involves developing procedures to identify messages that are worth studying given our resources for planned, labor-intensive research. Objectives: The primary objective of this study was to develop a workflow for finding a manageable number of clinically relevant messages from a much larger corpus of messages posted to an Internet mailing list, and to demonstrate the potential usefulness of our procedures for investigators by retrieving a set of messages tailored to the research question of a qualitative research team. Methods: We mined 14,576 messages posted to an Internet mailing list from April 2008 to May 2009. The list has about 450 subscribers, mostly dentists from North America interested in clinical practice. After extensive preprocessing, we used the Natural Language Toolkit to identify clinical phrases and keywords in the messages. Two academic dentists classified collocated phrases in an iterative, consensus-based process to describe the topics discussed by dental practitioners who subscribe to the list. We then consulted with qualitative researchers regarding their research question to develop a plan for targeted retrieval. We used selected phrases and keywords as search strings to identify clinically relevant messages and delivered the messages in a reusable database. Results: About half of the subscribers (245/450, 54.4%) posted messages. Natural language processing (NLP) yielded 279,193 clinically relevant tokens or processed words (19% of all tokens). Of these, 2.02% (5634 unique tokens) represent the vocabulary for dental practitioners. Based on pointwise mutual information score and clinical relevance, 325 collocated phrases (eg, fistula filled obturation and herpes zoster) with 108 keywords (eg, mercury) were classified into 13 broad categories with subcategories. In the demonstration, we identified 305 relevant messages (2.1% of all messages) over 10 selected categories with instances of collocated phrases, and 299 messages (2.1%) with instances of phrases or keywords for the category systemic disease. Conclusions: A workflow with a sequence of machine-based steps and human classification of NLP-discovered phrases can support researchers who need to identify relevant messages in a much larger corpus. Discovered phrases and keywords are useful search strings to aid targeted retrieval. We demonstrate the potential value of our procedures for qualitative researchers by retrieving a manageable set of messages concerning systemic and oral disease.


Social Networking:
Share |


Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Bekhuis, Ttcb24@pitt.eduTCB240000-0002-8537-9077
Kreinacke, M
Spallek, Hhspallek@pitt.eduHSPALLEK
Song, M
O'Donnell, JA
Centers: Other Centers, Institutes, Offices, or Units > Center for Dental Informatics
Date: 1 October 2011
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: Journal of Medical Internet Research
Volume: 13
Number: 4
DOI or Unique Handle: 10.2196/jmir.1799
Institution: University of Pittsburgh
Schools and Programs: School of Medicine > Biomedical Informatics
Refereed: Yes
MeSH Headings: Dental Informatics; Electronic Mail; Internet; Natural Language Processing
PubMed Central ID: PMC3236668
PubMed ID: 22112583
Date Deposited: 11 Feb 2013 15:37
Last Modified: 19 May 2022 14:18


Monthly Views for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item