当前位置： SCI文献检索 > BMC Medical Informatics and Decision Making期刊下所有文献 > An automatic method to generate domain-specific investigator networks using PubMed abstracts.

An automatic method to generate domain-specific investigator networks using PubMed abstracts.

Abstract：

BACKGROUND:Collaboration among investigators has become critical to scientific research. This includes ad hoc collaboration established through personal contacts as well as formal consortia established by funding agencies. Continued growth in online resources for scientific research and communication has promoted the development of highly networked research communities. Extending these networks globally requires identifying additional investigators in a given domain, profiling their research interests, and collecting current contact information. We present a novel strategy for building investigator networks dynamically and producing detailed investigator profiles using data available in PubMed abstracts. RESULTS:We developed a novel strategy to obtain detailed investigator information by automatically parsing the affiliation string in PubMed records. We illustrated the results by using a published literature database in human genome epidemiology (HuGE Pub Lit) as a test case. Our parsing strategy extracted country information from 92.1% of the affiliation strings in a random sample of PubMed records and in 97.0% of HuGE records, with accuracies of 94.0% and 91.0%, respectively. Institution information was parsed from 91.3% of the general PubMed records (accuracy 86.8%) and from 94.2% of HuGE PubMed records (accuracy 87.0). We demonstrated the application of our approach to dynamic creation of investigator networks by creating a prototype information system containing a large database of PubMed abstracts relevant to human genome epidemiology (HuGE Pub Lit), indexed using PubMed medical subject headings converted to Unified Medical Language System concepts. Our method was able to identify 70-90% of the investigators/collaborators in three different human genetics fields; it also successfully identified 9 of 10 genetics investigators within the PREBIC network, an existing preterm birth research network. CONCLUSION:We successfully created a web-based prototype capable of creating domain-specific investigator networks based on an application that accurately generates detailed investigator profiles from PubMed abstracts combined with robust standard vocabularies. This approach could be used for other biomedical fields to efficiently establish domain-specific investigator networks.

journal_name

BMC Med Inform Decis Mak

journal_title

BMC medical informatics and decision making

authors

Yu W,Yesupriya A,Wulf A,Qu J,Gwinn M,Khoury MJ

doi

10.1186/1472-6947-7-17

subject

Has Abstract

pub_date

2007-06-20 00:00:00

pages

issn

1472-6947

pii

1472-6947-7-17

journal_volume

pub_type

杂志文章

在线工具

An automatic method to generate domain-specific investigator networks using PubMed abstracts.