Exploring Novel Computable Knowledge in Structured Drug Product Labels.

This paper introduces a database derived from Structured Product Labels (SPLs). SPLs are legally mandated snapshots containing information on all drugs released to market in the United States. Since publication is not required for pre-trial findings, we hypothesize that SPLs may contain knowledge absent in the literature, and hence "novel." SemMedDB is an existing database of computable knowledge derived from the literature. If SPL content could be similarly transformed, novel clinically relevant assertions in the SPLs could be identified through comparison with SemMedDB. After we derive a database (containing 4,297,481 assertions), we compare the extracted content with SemMedDB for recent FDA drug approvals. We find that novelty between the SPLs and the literature is nuanced, due to the redundancy of SPLs. Highlighting areas for improvement and future work, we conclude that SPLs contain a wealth of novel knowledge relevant to research and complementary to the literature. ©2020 AMIA - All rights reserved.

Citation

Scott A Malec, Richard D Boyce. Exploring Novel Computable Knowledge in Structured Drug Product Labels. AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science. 2020;2020:403-412

PMID: 32477661

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