Aurotech supports four different Data
Analytics and Data Warehousing initiatives at the FDA: a Unified Data
Warehouse, a Data Lake as service in the cloud (AWS), SAP HANA in the Cloud
(AWS), and use of natural language processing (NLP) in medical trials over that
For the HHS Accelerate program we also used AI/ML for contracting writing. Aurotech is part of the IAAI (Intelligence Automation and Artificial Intelligence) government-approved list. To learn more, click HERE
With response to COVID-19 as a top priority, the FDA needed a solution to rapidly find valuable correlations and uncover insights across scientific publications on the types of treatments being prognosticated, administered, and assessed. We leveraged World Health Organization COVID-19 Open Research Dataset (CORD) repository of almost 200,000 scientific publications relevant to the pandemic. The CORD data sets are integrated with FDA’s SAP HANA backend that analyzes the text in memory using Natural Language Processing (NLP) and correlates between a custom biological dictionary and the NLM Medical Subject Headings (MeSH) dictionary. The result set is refined by other metadata, such as the location of the researcher, their affiliated organizations, and the date of publication. Aurotech created knowledge graphs that capture facts and relationships related to people, treatments, chemical compounds, drugs, and biologics.
FDA’s Office of Prescription Drug Promotion currently receives 125,000 unique promotional materials annually with receipts projected to increase 5% each year while reviewer staffing levels are projected to remain unchanged. Aurotech developed a solution to augment the reviewers with Docxonomy, an AI-based platform, to help automate the analysis of promotional materials and compare documents to ensure they remained unaltered to be consistent with FDA polices. We converted source materials contained in PDF documents to structured text and employed NLP toolkits to define risk categories such as boxed warning and contraindication information. We used AI modeling services to build and train ML algorithms that compare documents to ensure continuous process improvement.