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language: en
license: mit
task_categories:
- text-classification
tags:
- clinical-trials
- cascade-transition
- five-node-cascade
size_categories:
- 1K<n<10K
pretty_name: Clinical Five Node Shock Cascade Boundary
---
# 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
perfusion_pressure
physiological_buffer
intervention_delay
organ_coupling
metabolic_perfusion_failure
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.
metabolic_perfusion_failure 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 |
| metabolic_perfusion_failure | lactate, base deficit, venous oxygen saturation, acid-base status | rising lactate, worsening base deficit, poor tissue perfusion markers |
# Prediction target
label_shock_cascade
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:
scenario_id
perfusion_pressure
physiological_buffer
intervention_delay
organ_coupling
metabolic_perfusion_failure
label_shock_cascade
Values are normalized between 0 and 1 for training simplicity.
Lower perfusion_pressure increases risk.
Lower physiological_buffer increases risk.
Higher intervention_delay, higher organ_coupling, and higher metabolic_perfusion_failure 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
recall_cascade_detection
false_safe_rate
f1
confusion matrix
The primary metric is recall_cascade_detection because the main task is to detect approaching shock boundary failure rather than simply optimize overall accuracy.
false_safe_rate 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. |