ClarusC64/clinical-quad-renal-stress-buffer-lag-coupling-aki-transition-v0.1

What this repo does

This dataset models the transition from strained but recoverable renal physiology to acute kidney injury using a four-variable coupling structure.

The goal is to detect when a patient is drifting toward renal deterioration before overt kidney failure is established.

Acute kidney injury often unfolds as a cascade. Renal stress rises, physiological reserve narrows, intervention delays reduce reversibility, and organ interactions amplify decline.

The quad structure captures the core drivers of that transition.

Core quad

renalstress physiologicalbuffer interventiondelay organcoupling

renal_stress reflects the burden placed on kidney function.

physiological_buffer reflects reserve capacity and resilience against renal decline.

intervention_delay captures lag in fluids, nephrotoxin withdrawal, hemodynamic correction, renal review, or escalation.

organ_coupling reflects how renal dysfunction interacts with wider systemic instability.

Clinical Variable Mapping

Prediction target

labelakitransition

Binary classification.

0 = renal physiology remains stable or recoverable 1 = AKI transition is approaching

Binary simplification note

The Cascade Transition framework supports a full five-stage trajectory:

0 stable regime 1 deterioration drift 2 near cascade boundary 3 active cascade propagation 4 recovery trajectory

This v0.1 dataset intentionally uses a binary formulation.

Binary classification is easier to validate clinically and aligns with how AKI monitoring and escalation systems are deployed in practice. Operational systems usually require a clear alert condition rather than a multi-stage taxonomy.

The full five-stage structure remains part of the broader framework and may appear in future dataset versions.

Row structure

Each row represents a simulated patient state.

Columns:

scenarioid renalstress physiologicalbuffer interventiondelay organcoupling labelaki_transition

Values are normalized between 0 and 1 for training simplicity.

Higher renal_stress increases risk.

Lower physiological_buffer increases risk.

Higher interventiondelay and higher organcoupling increase risk.

Files

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

train.csv contains labeled rows.

tester.csv contains unlabeled rows with the same schema except for the target label.

scorer.py evaluates binary classification performance.

Evaluation

The scorer computes:

accuracy precision recallcascadedetection falsesaferate f1 confusion matrix

The primary metric is recallcascadedetection because the main task is to detect approaching renal deterioration rather than simply optimize overall accuracy.

falsesaferate captures the proportion of positive danger cases missed by the model.

License

MIT

Structural Note

This dataset is part of the Clarus Cascade Transition Dataset family.

These datasets model how complex systems move from stable regimes into cascading failure states.

In renal deterioration this corresponds to the transition from stressed but recoverable kidney function into coupled AKI cascade.

Production Deployment

Potential applications include:

AKI early warning ward deterioration monitoring ICU renal risk detection decision support for nephrotoxic exposure and fluid management

Enterprise & Research Collaboration

Clarus datasets explore stability boundaries in complex systems including clinical deterioration, infrastructure failure, financial contagion, and AI system stability.