Datasets:

ClarusC64
/

cascade-multi-ai-healthcare-triage-staffing-capacity-mortality-surge-v0.1

scenario_id string	ai_triage_reliance float64	staffing_shortage_index float64	bed_capacity_utilization float64	triage_error_rate float64	patient_inflow_velocity float64	icu_occupancy_rate float64	supply_chain_delay_days int64	escalation_protocol_lag_hours int64	clinical_override_rate float64	public_health_alert_lag_days int64	buffer_staff_reserve float64	buffer_bed_surge_capacity float64	cascade_severity_score float64	label_cascade_event int64
MED-001	0.34	0.28	0.62	0.05	0.41	0.58	6	4	0.22	3	0.71	0.68	0.36	0
MED-002	0.72	0.66	0.88	0.14	0.79	0.92	12	9	0.18	6	0.34	0.31	0.91	1
MED-003	0.48	0.42	0.74	0.08	0.56	0.71	8	6	0.24	4	0.58	0.55	0.57	0
MED-004	0.81	0.79	0.94	0.18	0.88	0.96	16	11	0.15	7	0.26	0.24	0.96	1
MED-005	0.61	0.55	0.83	0.11	0.67	0.85	10	8	0.2	5	0.49	0.46	0.71	0
MED-006	0.28	0.24	0.58	0.04	0.36	0.52	5	3	0.27	2	0.76	0.73	0.3	0
MED-007	0.9	0.88	0.97	0.22	0.93	0.99	20	14	0.12	8	0.2	0.18	0.99	1
MED-008	0.44	0.38	0.69	0.07	0.51	0.65	7	5	0.25	3	0.63	0.6	0.6	0
MED-009	0.76	0.71	0.91	0.16	0.84	0.95	14	10	0.17	7	0.29	0.27	0.94	1

What this repo does

This dataset tests whether a model can detect a healthcare cascade where AI-assisted triage interacts with staffing shortages, capacity strain, and delayed escalation to produce mortality surge risk.

You provide structured signals describing:

AI triage reliance and error rate
staffing shortage and bed utilization
patient inflow and ICU pressure
supply chain delay and escalation lag
public health alert timing and buffer capacity

The model predicts whether the interaction crosses into a cascade event.

Core cascade

Four interacting systems:

AI triage

ai_triage_reliance
triage_error_rate

Workforce and capacity

staffing_shortage_index
bed_capacity_utilization

Flow pressure

patient_inflow_velocity
icu_occupancy_rate

Escalation and governance

escalation_protocol_lag_hours
public_health_alert_lag_days

Prediction target

Target column:

label_cascade_event

Meaning:

0 = strain remains manageable
1 = multi-system cascade produces surge-level mortality risk

Row structure

Each row is a scenario snapshot.

Key columns:

ai_triage_reliance
staffing_shortage_index
bed_capacity_utilization
triage_error_rate
patient_inflow_velocity
icu_occupancy_rate
supply_chain_delay_days
escalation_protocol_lag_hours
clinical_override_rate
public_health_alert_lag_days
buffer_staff_reserve
buffer_bed_surge_capacity
cascade_severity_score

Files

data/train.csv
10-line labeled sample
data/tester.csv
10-line labeled sample
scorer.py
Binary metrics and confusion matrix

Evaluation

Run:

python scorer.py --gold data/tester.csv --pred your_predictions.csv

Outputs:

accuracy
precision
recall
f1
confusion matrix

License

MIT

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.

Downloads last month: 7

Number of rows:

Total file size:

9.5 kB