PhenoClusterΒΆ
A general-purpose framework for data-driven clinical phenotype discovery using Latent Class / Profile Analysis.
PhenoCluster is a Python framework for unsupervised discovery of clinical phenotypes from heterogeneous patient data. It implements an end-to-end pipeline: from data preprocessing and latent class identification to outcome association analysis, survival modelling, and multistate transition modelling.
The framework is domain-agnostic and can be applied to any clinical cohort study where the goal is to identify latent patient subgroups and characterise their relationship with clinical outcomes.
Getting Started
User Guide
- CLI Reference
- Configuration Reference
- global
- data
- preprocessing.row_filter
- preprocessing.imputation
- preprocessing.categorical_encoding
- preprocessing.outlier
- preprocessing.feature_selection
- model
- outcome
- stability
- survival
- multistate
- inference
- reference_phenotype
- external_validation
- cache
- visualization
- logging
- data_quality
- categorical_flow
- feature_characterization
- Configuration Profiles
Reference