CohortContrast

Enrichment Analysis of Clinically Relevant Concepts in Common Data Model Cohort Data

Identifies clinically relevant concepts in Observational Medical Outcomes Partnership Common Data Model cohorts using an enrichment-based workflow. Defines target and control cohorts and extracts medical interventions that are over-represented in the target cohort during the observation period. Users can tune filtering and selection thresholds. The workflow includes chi-squared tests for two proportions with Yates continuity correction, logistic tests, and hierarchy and correlation mappings for relevant concepts. The results can be optionally explored using the bundled graphical user interface. For workflow details and examples, see <https://healthinformaticsut.github.io/CohortContrast/>.

README

# CohortContrast Viewer Python Layout

This folder is organized by responsibility.

- `app.py`: application entrypoint.
- `config/`: constants and runtime configuration.
- `data/`: parquet loading, preprocessing, caching and filtering.
- `layout/`: Dash/Plotly UI layout composition.
- `callbacks/`: callback handlers split by feature area.
- `plots/`: plot building helpers.
- `clustering/`: clustering-specific computation.
- `precompute/`: summary precomputation pipeline.
- `models/`: state/registry models.
- `utils/`: generic utility helpers.

Guideline:
- If code reads/writes study data, put it in `data/`.
- If code creates UI nodes, put it in `layout/`.
- If code wires UI interactions, put it in `callbacks/`.
- If code only draws figures, put it in `plots/`.

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Dependencies (latest)