Label-to-MedDRA Mapping for Unlabeled Signal Prioritization
Maps label language to MedDRA terms to speed identification of potentially unlabeled case signals Evidence basis: FDA-associated evaluations showed NLP can map adverse-event terms in labels to MedDRA with practical precision and recall; shared-task results indicate strong triage support but not full replacement of expert safety review
The Problem
“Label-to-MedDRA Mapping for Unlabeled Signal Prioritization”
Organizations face these key challenges:
Maps label language to MedDRA terms to speed identification of potentially unlabeled case signals
Impact When Solved
The Shift
Human Does
- •Review product label language for adverse event terms
- •Manually map label terms to MedDRA concepts
- •Compare mapped terms against case signals to find potential gaps
- •Coordinate findings in spreadsheets and tracking documents
Automation
- •No AI-driven mapping support in the legacy workflow
- •No automated prioritization of potentially unlabeled signals
- •No continuous monitoring of mapping consistency
- •No system-generated triage recommendations
Human Does
- •Approve mapping rules and prioritization criteria
- •Review AI-flagged unlabeled signal candidates for clinical relevance
- •Resolve ambiguous or low-confidence term mappings
AI Handles
- •Extract adverse event language from product labels
- •Map label terms to MedDRA concepts and normalize variants
- •Prioritize potential unlabeled case signals based on mapping results
- •Flag low-confidence mappings and exceptions for human review
Operating Intelligence
How Label-to-MedDRA Mapping for Unlabeled Signal Prioritization runs once it is live
AI surfaces what is hidden in the data.
Humans do the substantive investigation.
Closed cases sharpen future detection.
Who is in control at each step
Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.
Step 1
Scan
Step 2
Detect
Step 3
Assemble Evidence
Step 4
Investigate
Step 5
Act
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI scans and assembles evidence autonomously. Humans do the substantive investigation. Closed cases improve future scanning.
The Loop
6 steps
Scan
Scan broad data sources continuously.
Detect
Surface anomalies, links, or emerging signals.
Assemble Evidence
Pull related records into a working case file.
Investigate
Humans interpret evidence and make case judgments.
Authority gates · 1
The system must not approve final MedDRA mappings for ambiguous or low-confidence label terms without review by a qualified safety reviewer. [S1]
Why this step is human
Investigative judgment involves ambiguity, legal considerations, and stakeholder impact that require human expertise.
Act
Carry out the human-directed next step.
Feedback
Closed investigations improve future detection.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in Label-to-MedDRA Mapping for Unlabeled Signal Prioritization implementations: