Curation and AI model development.
Custom AI models for clinical concept extraction, variable imputation, and clinical inference, built on rigorously curated reference standard data and engineered for measurable, regulatory-grade accuracy.
Models that turn medical records into research-ready variables.
Using the Verantos Curation Platform, our team builds and validates models tailored to your study variables, with accuracy as the headline metric, not an afterthought.
Concept extraction
Identify clinical concepts from structured and unstructured records: diagnoses, medications, biomarkers, findings.
Variable imputation
Fill missing values with statistically and clinically grounded estimates derived from the full record.
Clinical inference
Derive complex variables that require synthesizing evidence across the record: severity, response, eligibility.
Model accuracy is the product.
In pharmacoepidemiology, model error becomes measurement error, and measurement error becomes biased evidence.
Every Verantos model is evaluated against a held-out reference standard and reported with the metrics regulators and reviewers expect.
Dual-read, adjudicated records the model never trained on.
The trained model reads the same records, alone.
- Per-variable sensitivity, specificity, PPV, and F1
- Held-out reference standard validation
- Reliability measurement aligned with FDA guidance
- Transparent error analysis and failure modes
A rigorous reference standard process.
Models are only as trustworthy as the data they’re trained on. Our reference standard process uses two independent clinician annotators per record and only accepts labels that achieve high inter-rater reliability, ensuring training data is faithful to clinical truth.
Clinician annotator
Independent labeling
Clinician or AI agent
Independent labeling · accelerated option
Choose the pace of model development.
Both options use the same reference standard rigor and the same acceptance criteria. The accelerated option introduces an AI agent as one of the two annotators, increasing throughput while preserving inter-rater reliability requirements.
Dual clinician annotation
Two clinician annotators independently label every record in the reference sample. Adjudication and IRR thresholds gate the reference standard before any model sees it.
- Highest assurance for novel or high-risk variables
- Ideal for regulatory submissions
- Establishes the gold standard for all comparisons
Clinician + AI agent annotation
One clinician annotator paired with an AI agent annotator. Same inter-rater reliability gate, dramatically faster reference standard creation and model iteration.
- Faster time-to-model for large variable sets
- Same IRR acceptance threshold as standard
- Clinician retains adjudication authority
From variable definition to validated model.
1
Define
Variable specifications, clinical definitions, and acceptance criteria.
2
Label
Dual annotation on the Verantos Curation Platform.
3
Adjudicate
Resolve disagreements; verify inter-rater reliability.
4
Train
Train extraction, imputation, or inference models.
5
Validate
Hold-out evaluation with full accuracy reporting.
Built on the Verantos Curation Platform. Every engagement runs on the same labeling, training, and validation infrastructure that powers our curated data sets.
Ready to advance your evidence program?
Speak with our team about how the Verantos pharmacoepidemiology stack can accelerate your timeline.