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One of our big pharmaceutical clients needed a custom solution that would enable them to aggregate and index data from a variety of documents to be able to augment, assemble, curate, and use healthcare datasets. They were looking for an automated solution for extracting preclinical and clinical data from published literature, as well as other sources.
Arrayo’s solution leveraged machine learning methodology and artificial intelligence-based approaches, learning from prior data, and continuously improving through using a set of QC metrics. By using advanced NLP algorithms, we improved data extraction accuracy. This approach allowed for high-quality and enhanced resolution of ambiguous terms, synonyms, abbreviations, and lengthy text constructs that identify distant relationships between terms.
The software addressed several challenges encountered by our client’s team on a day-to-day basis, including:
Arrayo’s automated solution established reproducible and reliable generation of data directly from the incoming documents, which allowed our client to eliminate the reliance on vendor data products and reduce labor and licensing costs of the current data acquisition processes.