Update README.md

README.md

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----
-license: mit
----
+---
+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.