Developing timely insights into comparative effectiveness research with a text-mining pipeline

Area of Science:

• Biomedical Informatics
• Clinical Trial Data Management
• Health Services Research

Background:

• Comparative Effectiveness Research (CER) is crucial for informing healthcare decisions.
• CER data is sourced from retrospective analyses and prospective clinical trials.
• Existing data sources are fragmented, requiring efficient integration methods.

Purpose of the Study:

• To develop a text-mining pipeline using Natural Language Processing (NLP).
• To extract key information from diverse clinical trial data sources.
• To create an integrated, structured output for CER.

Main Methods:

• Utilized NLP techniques for information extraction.
• Integrated data from NIH ClinicalTrials.gov, WHO ICTRP, and Citeline Trialtrove.
• Employed tailored terminologies to identify comparative therapy trials.

Main Results:

• Developed a pipeline capable of processing multiple trial data sources.
• Generated structured output capturing comparative pharmaceutical trials.
• Enabled timely alerts for emerging clinical research.

Conclusions:

• The NLP pipeline offers an efficient method for CER data aggregation.
• Provides the earliest and most complete overview of emerging clinical research.
• Supports informed decision-making for healthcare providers, payers, and pharmaceutical companies.