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ClarusC64/clinical-five-node-shock-cascade-boundary-v0.1

What this repo does This dataset models the transition from pressured but recoverable circulation to shock cascade using a five-variable interaction structure. The goal is to detect when a patient is drifting toward shock boundary failure before overt systemic collapse is fully established. Shock often unfolds as a cascade: perfusion pressure falls, physiological reserve narrows, intervention delays reduce reversibility, organ interactions amplify instability, and metabolic… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-five-node-shock-cascade-boundary-v0.1.

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Dataset Card

What this repo does

This dataset models the transition from pressured but recoverable circulation to shock cascade using a five-variable interaction structure.

The goal is to detect when a patient is drifting toward shock boundary failure before overt systemic collapse is fully established.

Shock often unfolds as a cascade: perfusion pressure falls, physiological reserve narrows, intervention delays reduce reversibility, organ interactions amplify instability, and metabolic perfusion failure reduces tissue-level recoverability.

The five-node structure extends the core Clarus clinical logic beyond a quad and captures a higher-order cascade surface.

Core five-node structure

perfusionpressure physiologicalbuffer interventiondelay organcoupling metabolicperfusionfailure

perfusion_pressure reflects circulatory adequacy and tissue perfusion.

physiological_buffer reflects reserve capacity and resilience against hypotension and systemic stress.

intervention_delay captures lag in fluids, vasopressors, transfusion, source control, or escalation.

organ_coupling reflects how dysfunction in one organ system begins driving instability in others.

metabolicperfusionfailure reflects the downstream tissue-level consequences of impaired circulation and inadequate oxygen delivery.

Clinical Variable Mapping

Node	Clinical Measurements	Typical Risk Signals
perfusion_pressure	MAP, systolic blood pressure, shock index, capillary refill	MAP < 65, SBP < 90, shock index rising, delayed refill
physiological_buffer	age, albumin, baseline reserve, frailty, comorbidity burden	low reserve, low albumin, frailty
intervention_delay	delay in fluids, vasopressors, transfusion, source control, escalation	delayed resuscitation, delayed escalation
organ_coupling	urine output, mental status decline, SOFA interaction, respiratory spillover	worsening multi-organ interaction, oliguria, reduced responsiveness
metabolicperfusionfailure	lactate, base deficit, venous oxygen saturation, acid-base status	rising lactate, worsening base deficit, poor tissue perfusion markers

Prediction target

labelshockcascade

Binary classification.

0 = circulatory state remains stable or recoverable 1 = shock cascade boundary 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 shock monitoring and escalation systems are used 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.

Why five nodes here

Most of the clinical suite uses quad structure because quad coupling is easier to validate, explain, and deploy.

This repo is a deliberate flagship extension.

The additional fifth node captures a clinically decisive layer that often determines whether shock becomes irreversible: tissue-level metabolic perfusion failure. That makes this dataset suitable as an advanced boundary set inside the wider clinical suite.

Row structure

Each row represents a simulated patient state.

Columns:

scenarioid perfusionpressure physiologicalbuffer interventiondelay organcoupling metabolicperfusionfailure labelshock_cascade

Values are normalized between 0 and 1 for training simplicity.

Lower perfusion_pressure increases risk.

Lower physiological_buffer increases risk.

Higher interventiondelay, higher organcoupling, and higher metabolicperfusionfailure 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 shock boundary failure 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 shock physiology this corresponds to the transition from pressured but compensating circulation into systemic perfusion collapse.

This five-node dataset functions as an advanced boundary set within the clinical suite.

Production Deployment

Potential applications include:

shock boundary detection ICU hemodynamic monitoring advanced clinical deterioration modeling decision support for unstable circulatory patients research prototypes for higher-order cascade detection

Enterprise & Research Collaboration

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