Datasets:
id string | context string | step_unit string | pressure_t0 float64 | pressure_t1 float64 | pressure_t2 float64 | pressure_t3 float64 | buffer_t0 float64 | buffer_t1 float64 | buffer_t2 float64 | buffer_t3 float64 | lag_t0 float64 | lag_t1 float64 | lag_t2 float64 | lag_t3 float64 | coupling_t0 float64 | coupling_t1 float64 | coupling_t2 float64 | coupling_t3 float64 | cross_step int64 | notes string | label_cascade_state int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ICU-0001 | Stable ICU patient. Vitals steady. Low vasopressor need. Labs monitored. | hours | 0.22 | 0.26 | 0.28 | 0.3 | 0.86 | 0.84 | 0.82 | 0.8 | 0.18 | 0.2 | 0.22 | 0.24 | 0.3 | 0.34 | 0.36 | 0.38 | 0 | stable trajectory | 0 |
ICU-0002 | Rising oxygen requirement. Lactate mildly up. Rapid review and protocol started. | hours | 0.34 | 0.4 | 0.44 | 0.46 | 0.78 | 0.74 | 0.72 | 0.7 | 0.22 | 0.24 | 0.26 | 0.28 | 0.34 | 0.38 | 0.4 | 0.42 | 0 | recoverable | 0 |
ICU-0003 | Worsening hypotension. Lactate rising. Escalation delayed due to handoff. | hours | 0.48 | 0.6 | 0.72 | 0.84 | 0.7 | 0.6 | 0.46 | 0.34 | 0.28 | 0.44 | 0.66 | 0.84 | 0.42 | 0.58 | 0.74 | 0.88 | 2 | shock formation | 1 |
ICU-0004 | Sepsis suspected. Antibiotics delayed. Imaging backlog. Pressors increasing. | hours | 0.52 | 0.64 | 0.76 | 0.88 | 0.66 | 0.54 | 0.4 | 0.28 | 0.32 | 0.54 | 0.74 | 0.88 | 0.5 | 0.66 | 0.8 | 0.9 | 2 | cross t1-t2 | 1 |
ICU-0005 | Respiratory failure worsens. Vent settings escalate. Renal function drops. Team coordination tight. | hours | 0.56 | 0.7 | 0.82 | 0.92 | 0.62 | 0.5 | 0.34 | 0.22 | 0.36 | 0.6 | 0.82 | 0.92 | 0.56 | 0.72 | 0.88 | 0.94 | 1 | early crossing | 1 |
ICU-0006 | Early sepsis bundle triggered. Fluids and pressors adjusted fast. Buffer restored. | hours | 0.44 | 0.52 | 0.5 | 0.48 | 0.72 | 0.76 | 0.78 | 0.8 | 0.3 | 0.26 | 0.22 | 0.2 | 0.46 | 0.44 | 0.4 | 0.38 | 0 | intervention holds | 0 |
ICU-0007 | Monitoring gap overnight. Rising lactate missed. Delayed escalation. Coupling tightens across teams. | hours | 0.5 | 0.62 | 0.78 | 0.9 | 0.68 | 0.56 | 0.38 | 0.26 | 0.28 | 0.5 | 0.76 | 0.9 | 0.48 | 0.64 | 0.82 | 0.92 | 1 | overnight drift | 1 |
ICU-0008 | Deterioration detected at t1. Senior review at t2. Source control planned. Coupling reduces. | hours | 0.46 | 0.58 | 0.6 | 0.56 | 0.66 | 0.62 | 0.68 | 0.7 | 0.4 | 0.34 | 0.28 | 0.24 | 0.52 | 0.5 | 0.46 | 0.42 | 0 | recovery path | 0 |
ICU-0009 | Refractory shock develops. Escalation delayed. Multi-organ support tightens dependency loops. | hours | 0.58 | 0.72 | 0.86 | 0.94 | 0.6 | 0.48 | 0.32 | 0.2 | 0.34 | 0.62 | 0.86 | 0.94 | 0.58 | 0.74 | 0.9 | 0.96 | 2 | lock-in timing visible | 1 |
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.
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