ClarusC64/clinical-quad-renal-stress-buffer-lag-coupling-aki-transition-v0.3

What this repo does

This dataset models acute kidney injury transition using the Clarus quad coupling framework combined with trajectory and system dynamics.

The goal is to predict whether a patient is approaching an AKI deterioration cascade.

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 quad

renalstress physiologicalbuffer interventiondelay organcoupling

These four variables represent the interacting physiological state controlling renal stability.

renal_stress Represents physiological load on the kidneys such as ischemia, toxin exposure, or systemic inflammation.

physiological_buffer Represents patient reserve including metabolic and cardiovascular compensation.

intervention_delay Represents the delay between deterioration onset and clinical response.

organ_coupling Represents cross-organ interaction such as cardio-renal or sepsis-renal coupling.

The Clarus quad 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.

driftvelocity — speed of motion through state space driftacceleration — 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

driftacceleration(t) = driftvelocity(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

labelakitransition

Binary classification.

1 indicates the system is entering an AKI deterioration cascade.

0 indicates the system remains recoverable.

Binary simplification note

Real AKI deterioration unfolds as a continuous physiological process.

This dataset encodes boundary crossing 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 core quad.

Example

renalstress 0.43 physiologicalbuffer 0.72 interventiondelay 0.21 organcoupling 0.26 driftgradient +0.66 driftvelocity 0.18 driftacceleration +0.08 boundarydistance 0.07 labelakitransition 1

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

However:

driftgradient shows motion toward deterioration driftvelocity shows active movement through state space driftacceleration shows increasing momentum boundarydistance 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

scenarioid renalstress physiologicalbuffer interventiondelay organcoupling driftgradient driftvelocity driftacceleration boundarydistance labelaki_transition

Files

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

Evaluation

Models are evaluated using binary classification metrics.

accuracy precision recallcascadedetection falsesaferate f1 confusion_matrix

Primary metric

recallcascadedetection

Secondary diagnostic metric

falsesaferate

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.