Monitoring scientific literature at scale is both a data engineering and intelligence challenge.
A modern publication-monitoring pipeline can bring together scientific APIs, preprint feeds, clinical-trial registries, and regulatory sources, then use ETL pipelines, deduplication, biomedical NLP, relevance scoring, and human-in-the-loop validation to transform raw records into actionable intelligence.
The approach is particularly relevant for teams working across pharmaceutical research, Medical Affairs, competitive intelligence, clinical evidence, and safety monitoring.
The case study explores the architecture, data sources, NLP strategy, deduplication framework, dashboards, APIs, technology stack, and implementation roadmap behind a scalable solution.
Read the full case study:
https://www.apothiumai.com/Case-Studies/medical-affairs-publication-monitoring
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