Datasets:
language: en
license: mit
task_categories:
- text-classification
tags:
- clinical-trials
- temporal-cascade
- five-node-cascade
size_categories:
- 1K<n<10K
pretty_name: 'Clinical ICU Temporal Cascade: Deterioration to Shock Lock-in'
What this repo does
This dataset tests whether a model can detect an ICU deterioration cascade forming over time by reading a short ordered window of signals and predicting whether shock lock-in occurs by the final step.
Core quad
pressure
buffer
lag
coupling
Prediction target
label_cascade_state
Row structure
One row represents one short time window (t0 to t3) for an ICU patient under deterioration pressure. It includes time-series values for physiologic pressure, reserve buffer, response lag, and care-pathway coupling tightness. The label marks whether cascade lock-in is reached by t3.
Files
data/train.csv
data/tester.csv
scorer.py
Evaluation
Run predictions on tester.csv.
Score with scorer.py.
License
MIT
Structural Note
This dataset identifies a measurable coupling pattern associated with systemic instability. The sample demonstrates the geometry. Production-scale data determines operational exposure.
What Production Deployment Enables
• 50K–1M row datasets calibrated to real operational patterns • Pair, triadic, and quad coupling analysis • Real-time coherence monitoring • Early warning before cascade events • Collapse surface and recovery window modeling • Integration and implementation support
Small samples reveal structure. Scale reveals consequence.
Enterprise & Research Collaboration
Clarus develops production-scale coherence monitoring infrastructure for critical systems across healthcare, finance, infrastructure, and regulatory domains.
For dataset expansion, custom coherence scorers, or deployment architecture: team@clarusinvariant.com
Instability is detectable. Governance determines whether it propagates.