In this free webinar, gain insights into moving messy clinical documents into trustworthy features while preserving human judgment, auditability and speed. Attendees will learn about a step-by-step PDF-to-OMOP pattern that uses agents for speed and humans for judgment. The featured speaker will discuss the safe use of agents for intake script editing and feature engineering within constraints. Attendees will gain insights into early detection and mitigation of common intake failure modes.
TORONTO, Sept. 24, 2025 /PRNewswire/ — Clinical data rarely arrives analysis-ready. In a recent project, our team used bfPREP™ to convert 10,000+ pages of clinical PDFs into an OMOP-structured dataset suitable for analysis and machine learning using bfLEAP™. 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 bfPREP™, 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.
Examples include categorizing diseases, medications, lesion descriptors and normalizing free text into intended categories. Throughout, we prioritize medium-complexity repeatables that a human can verify quickly. We will cover failure modes we encountered, such as clipped layouts and column ambiguity, and the guardrails that kept them contained. Attendees will leave with templates, patterns and decision criteria to stand up agent-powered intake without hype. The goal is not full automation. It is verified automation that moves messy clinical documents into trustworthy features while preserving human judgment, auditability and speed.
Join Juan Felipe Beltrán Lacouture, PhD, Director of AI, Machine Learning & Innovation, BullFrog AI, for the live webinar on Monday, October 6, 2025, at 11am EDT (5pm CEST/EU-Central).
For more information or to register for this event, visit Clinical Data Analysis with Agents: Reliable Automation with Human Oversight.
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