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Community and topic modeling for infectious disease clinical trial recommendation
Magdalyn E Elkin1, Xingquan Zhu1
1Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431 USA.
Summary
Network analysis of clinical trial data reveals trends in infectious disease research. This approach aids in summarizing research and recommending trials to sponsors and investigators.
Area of Science:
- Medical Informatics
- Network Science
- Infectious Diseases
Background:
- ClinicalTrials.gov is a valuable data source for medical research.
- Limited analytical studies exist for understanding clinical trial data.
- Analyzing this data can reveal emerging research trends.
Purpose of the Study:
- To analyze infectious disease clinical trial research using network analysis.
- To understand the characteristics and concentrations of this research.
- To recommend clinical trials to sponsors and investigators.
Main Methods:
- Utilized network analysis on 4228 clinical trials, 4864 sponsors, and 1879 Medical Subject Heading (MeSH) keywords.
- Developed a community-topic-based link prediction approach.
- Modeled clinical trial sponsors into communities and keywords into topics.
Main Results:
- Identified sponsor communities with coherent interests.
- Extracted topics with semantically consistent keywords.
- Combined communities and topics for accurate clinical trial recommendation.
Conclusions:
- Network analysis significantly enhances the understanding of clinical trial research.
- This method aids in effective summarization, characterization, and prediction of research trends.
- The approach facilitates better matching of sponsors and investigators to relevant trials.
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