Data Mining of Cancer Vaccine Trials
Author Information
Author(s): Cao Xiaohong, Maloney Karen B, Brusic Vladimir
Primary Institution: Babson College, Wellesley, MA, USA; Cancer Vaccine Center, Dana-Farber Cancer Institute, Boston, MA, USA
Hypothesis
Can data mining techniques improve the analysis of cancer vaccine trials?
Conclusion
The study developed a data mining approach that allows for rapid extraction and visualization of complex data from clinical trial repositories, aiding in informed decision-making for future cancer vaccine trials.
Supporting Evidence
- The study focused on 645 clinical trials related to cancer vaccines.
- It identified neglected cancers such as bladder, liver, and pancreatic cancers.
- The application allows for rapid extraction of information about clinical trials.
Takeaway
This study created a tool to quickly gather and show information about cancer vaccine trials, helping researchers see what types of cancers are being studied and which ones are being overlooked.
Methodology
Data was extracted from ClinicalTrials.gov and analyzed using a data mining application for summarization and visualization.
Potential Biases
Potential publication bias may exist as positive results are published more readily than negative ones.
Limitations
The study did not include all fields from the clinical trial data, such as age criteria and specific vaccine technology.
Digital Object Identifier (DOI)
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