Examples demonstrating DeepCausality applications in biomedical and life sciences domains.
Run any example from the repository root:
cargo run -p medicine_examples --example <example_name>| Example | Domain | Description |
|---|---|---|
| aneurysm_risk | Cardiovascular | Aneurysm rupture risk via fluid fatigue accumulation |
| diving_decompression | Hyperbaric | Bühlmann ZH-L16C decompression with CNS O2 toxicity |
| epilepsy | Neurology | Virtual surgery planning using brain network digital twins |
| protein_folding | Biophysics | Non-Markovian protein dynamics with memory kernels |
| tissue_classification | Medical Imaging | Topological tissue analysis for tumor detection |
| tumor_treatment | Oncology | Optimizing TTFields therapy with Geometric Algebra |
Many biological systems exhibit memory effects - current behavior depends on history, not just the present state. The Generalized Master Equation (GME) captures this:
P(t+Δt) = T · P(t) + Σ K(τ) · P(t-τ)
Structural features (holes, voids) in tissue can indicate pathology. The Euler Characteristic provides a robust topological classifier.
- Drug Discovery: Understanding protein misfolding (Alzheimer's, Parkinson's)
- Tumor Detection: Identifying necrotic cores via topology
- Bioengineering: Designing proteins with specific functions
- Medical Imaging: Robust tissue classification
| Crate | Purpose |
|---|---|
deep_causality_physics |
Generalized Master Equation |
deep_causality_tensor |
Transition matrices, point clouds |
deep_causality_topology |
Simplicial complexes, TDA |
deep_causality_core |
Monadic effects |
- physics_examples - Pure physics simulations
- case_study_icu_sepsis - Clinical sepsis prediction