Across the healthcare industry, researchers and clinicians need to base decisions on the best possible view of data. Natural language processing-based text mining enables researchers to gather important insights from vast amounts of published information. Use cases range from drug discovery, clinical trial development, drug safety monitoring, through to real world insights and competitive intelligence.
To capture the landscape of information needed for a particular project, mining abstracts and full-text papers both bring benefits. Scientific abstracts tend to be short concise summaries, but miss much of the richness, detail, and granularity available from the full-text papers, particularly in tables.
“Using abstracts rather than full-text articles when text mining scientific articles may often be a false economy,” said Stephen Garfield in Text Mining Scientific Articles: Why You Need the Full Picture. “It may feel like the quick route to results, but in reality, only a full-text version is comprehensive. Put simply, text mining on full-text offers more facts, more kinds of facts and quicker paths to insights.”
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