Clinical data rarely arrives analysis-ready. In a recent project, our team used bfPREPTM to convert 10,000+ pages of clinical PDFs into an OMOP-structured dataset suitable for analysis and machine learning using bfLEAPTM. The organizing idea was simple: use agents as the connective tissue between intake and analysis, and keep a human in the loop wherever judgment, safety or nuance are required.
This session walks through a practical blueprint. By using bfPREPTM, first, the primary parsing task of PDF-to-CSV which covers triage of documents, schema generation and iterative text extraction. Second, tooling for entity resolution to align concepts to OMOP vocabularies (e.g., SNOMED CT, RxNorm, LOINC), with confidence thresholds that trigger human review. Third, templates for agents that edit existing schema-constrained loaders and transformation snippets for OMOP tables. With a verified core in place, we perform feature engineering using OMOP-compatible tools, then extend with agentic assists.
Learn more here: https://www.bullfrogai.com/bfprep