Rapid ML-Ready
Step 1 of 5

Study Setup

Configure your database, study period, methodology, and data model.

Workflow: Configure database and methodology here, then define your study criteria (step 2), time windows (step 3), covariates (step 4), and generate (step 5).

Future: i2b2, FHIR, PCORnet

How to structure patient rows

Generated script type

Study Definition

Define who enters the cohort, what outcome to predict, and optional exclusions or confounders. Each row is one piece of clinical evidence.

Cohort Entry

Who enters the study? Add one or more evidence rows (diagnosis, lab, drug, procedure).

Outcome

What event to predict in the follow-up window?

Exclusions (optional)

Patients matching any exclusion row are removed from the study.

Confounders (optional)

Risk factors tracked as binary columns in the output dataset.

Time Windows & Censoring

Define baseline and outcome window lengths.

How far back to look for patient history (e.g. 90 = 3 months, 365 = 1 year)

How far forward to look for outcome events (e.g. 180 = 6 months, 365 = 1 year)

Covariates (Features)

Select which features to include in your ML-ready dataset.

Demographics
Baseline Event Counts
Baseline Lab Values

Most recent in baseline period. Verify these are in your OMOP database.

Prior Event History
Custom Covariates (Any Concept ID)

Add any OMOP concept as a covariate. Choose a domain, enter the concept ID, pick how to aggregate, and give it a column name.

Review & Generate

Review your configuration. The self-check panel updates automatically.

Configuration Summary

Downloads a timestamped zip with study.sql, README.md, run.py, and optional artifacts