README.md

---
language: en
license: mit
task_categories:
- text-classification
tags:
- clinical-trials
- five-node-cascade
size_categories:
- 1K<n<10K
pretty_name: Clinical Five Node Shock Cascade Boundary Forecast
---

# What this repo does

This dataset models shock cascade boundary approach using a Clarus five-node coupling framework combined with trajectory and system dynamics.

The goal is to predict whether a patient is approaching the shock cascade boundary.

The dataset introduces a dynamic forecasting layer that allows models to reason about motion through the stability manifold rather than relying only on static physiological snapshots.

# Core five-node cascade

hemodynamic_stress  
buffer_capacity  
intervention_delay  
organ_coupling  
perfusion_stability  

These five variables represent the interacting physiological state controlling shock stability.

hemodynamic_stress  
Represents circulatory burden such as falling pressure reserve, rising vasopressor demand, tissue hypoperfusion, or escalating shock load.

buffer_capacity  
Represents patient reserve including metabolic resilience, cardiovascular compensation, and remaining tolerance to hemodynamic stress.

intervention_delay  
Represents the delay between deterioration onset and effective clinical response.

organ_coupling  
Represents cross-system interaction such as cardio-renal stress, inflammatory spillover, respiratory-hemodynamic linkage, or multi-organ destabilization.

perfusion_stability  
Represents the remaining stability of tissue perfusion, circulatory adequacy, and microvascular resilience under shock stress.

The five-node structure models how these variables interact to produce either recoverable dynamics or cascade deterioration.

# Trajectory layer

drift_gradient represents the direction of motion in the system state space.

Values near +1 indicate motion toward instability.

Values near −1 indicate motion toward recovery.

This variable captures trajectory alignment with the instability boundary.

# Dynamic forecasting layer

Three additional variables describe how the system moves through the stability manifold.

drift_velocity — speed of motion through state space  
drift_acceleration — change in velocity across consecutive time steps  
boundary_distance — proximity to the instability boundary  

Together these variables allow models to estimate how rapidly instability is approaching rather than simply identifying its direction.

This converts the dataset from trajectory detection into dynamic cascade forecasting.

# Dynamic variable definitions

drift_velocity

Magnitude of state change between consecutive time steps.

Definition

drift_velocity(t) = ||x(t) − x(t−1)||

Interpretation

Higher values indicate faster movement through the stability manifold.

Lower values indicate slower system evolution.

drift_acceleration

Rate of change of drift velocity across three consecutive snapshots.

Definition

drift_acceleration(t) = drift_velocity(t) − drift_velocity(t−1)

where

drift_velocity(t) = ||x(t) − x(t−1)||

Interpretation

Positive values indicate accelerating movement toward instability.

Negative values indicate deceleration or stabilization.

boundary_distance

Weighted metric distance between the current system state and the instability boundary.

Definition

Computed as weighted Euclidean distance from the current state vector to the nearest point on the instability boundary, normalized to the range 0 to 1.

Interpretation

0 indicates the system has reached the cascade boundary.

Lower values indicate minimal remaining stability margin.

Higher values indicate greater separation from collapse.

# Prediction target

label_shock_cascade_boundary

Binary classification.

1 indicates the system is entering the shock cascade boundary regime.

0 indicates the system remains recoverable.

# Binary simplification note

Real shock deterioration unfolds as a continuous physiological process.

This dataset encodes boundary approach as a binary classification problem to simplify model evaluation and benchmarking.

# False stability example

The central challenge in this dataset is detecting cases that appear stable when viewed only through the five-node state variables.

Example

hemodynamic_stress 0.44  
buffer_capacity 0.72  
intervention_delay 0.21  
organ_coupling 0.27  
perfusion_stability 0.70  
drift_gradient +0.66  
drift_velocity 0.18  
drift_acceleration +0.08  
boundary_distance 0.07  
label_shock_cascade_boundary 1  

This row appears relatively safe if only the static state variables are considered.

However:

drift_gradient shows motion toward deterioration  
drift_velocity shows active movement through state space  
drift_acceleration shows increasing momentum  
boundary_distance shows very little remaining stability margin  

This is a false stability case.

The patient appears stable in a static snapshot but is dynamically close to deterioration.

# Row structure

scenario_id  
hemodynamic_stress  
buffer_capacity  
intervention_delay  
organ_coupling  
perfusion_stability  
drift_gradient  
drift_velocity  
drift_acceleration  
boundary_distance  
label_shock_cascade_boundary  

# Files

data/train.csv  
data/tester.csv  
scorer.py  
readme.md  

# Evaluation

Models are evaluated using binary classification metrics.

accuracy  
precision  
recall_cascade_detection  
false_safe_rate  
f1  
confusion_matrix  

Primary metric

recall_cascade_detection

Secondary diagnostic metric

false_safe_rate

The primary goal is detecting cascade onset rather than maximizing overall accuracy.

# License

MIT

# Structural Note

Clarus datasets encode cascade instability through interacting system variables rather than isolated predictors.

Collapse emerges from coupled system dynamics rather than from any single measurement crossing a threshold.

# Production Deployment

These datasets support early warning models designed to detect deterioration trajectories before irreversible cascade occurs.

Such models may assist clinical monitoring systems by identifying dynamic instability patterns earlier than threshold-based alerts.

# Enterprise & Research Collaboration

The Clarus dataset framework can be applied across multiple domains including clinical medicine, infrastructure monitoring, complex AI systems, and other environments where cascade instability must be detected before boundary crossing.

For dataset expansion, custom coherence scorers, or deployment architecture:  
team@clarusinvariant.com

Instability is detectable.  
Governance determines whether it propagates.