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Using ProMED-Mail and MedWorm blogs for cross-domain pattern analysis in epidemic intelligence
Avaré Stewart1, Kerstin Denecke
1L3S Research Center, Hannover, Germany. stewart@L3S.de
Studies in Health Technology and Informatics
|September 16, 2010
Summary
Medical blogs can aid disease surveillance by identifying disease reporting events. A new framework effectively extracts these events from noisy blog data, improving biosurveillance efforts.
Area of Science:
- Computational epidemiology
- Public health informatics
- Natural language processing
Background:
- User-generated content from medical blogs offers a valuable, albeit noisy, data source for public health surveillance.
- Traditional biosurveillance methods can be augmented by analyzing online health discussions.
Purpose of the Study:
- To develop and evaluate a framework for automatically inferring disease reporting event extraction patterns from noisy medical blog content.
- To enhance biosurveillance investigations by leveraging user-generated health data.
Main Methods:
- Developed a Cross-Domain Pattern Analysis Framework to align disease reporting sentences in blogs with outbreak reports.
- Utilized sublanguage analysis of outbreak reports to guide pattern extraction from blog text.
- Compared Phase-Level sequences with Word-Level sequences for cross-domain alignment.
Main Results:
- Phase-Level sequences demonstrated greater overlap across domains compared to Word-Level sequences.
- The cross-domain alignment process effectively filtered noisy data from blogs.
- Identified robust candidate sequence patterns for disease event extraction from abundant text.
Conclusions:
- User-generated content in medical blogs is a viable resource for biosurveillance.
- The proposed framework successfully extracts disease reporting events from noisy blog data.
- Phase-Level sequence analysis offers a promising approach for cross-domain text analysis in public health.
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