ClarusC64/clinical-staffing-patient-acuity-coherence-risk-v0.2

Clinical Staffing Patient Acuity Coherence Risk v0.2

What this is

A small dataset that tests one question:

Can you detect when a system is moving toward failure, not just under pressure?

This repo focuses on staffing stability in a clinical setting.

But the structure applies to any system where:

• demand rises
• capacity lags
• coordination breaks

Run this first

Generate baseline predictions:

python baseline_heuristic.py data/tester.csv predictions.csv

Score them:

python scorer.py data/tester.csv predictions.csv

That is enough to see the full evaluation loop.

You will get:

standard metrics

trajectory detection performance

failure detection errors

What to try next

Replace the baseline.

Build your own model.

Output a file like:

id,prediction_score
0,0.12
1,0.81
2,0.67

Then run:

python scorer.py data/tester.csv your_predictions.csv
What matters

Not just accuracy.

The key signals are:

recall_trajectory_deterioration_detection

false_stable_trajectory_rate

These tell you:

are you catching systems that are getting worse

are you missing hidden failure

Data

Each row represents a system state.

Core variables:

staffing_capacity

patient_acuity_index

coherence_risk_score

response_delay_min

staffing_margin_ratio

acuity_acceleration_index

handover_friction_score

coordination_stability_score

drift_gradient

Target:

label_staffing_acuity_coherence_failure

Important distinction

There are two different components in this repo.

scorer.py

evaluates predictions

domain-agnostic

works across all v0.2 datasets

does not generate predictions

baseline_heuristic.py

generates predictions

domain-specific

uses the variables in this dataset

Do not reuse the heuristic across datasets.

It is only a local reference.

What changed from v0.1

v0.1:

static classification

v0.2:

adds direction via drift_gradient

This allows you to separate:

stressed but stable systems

stressed and deteriorating systems

Why this exists

Most models answer:

what is happening now

This tests:

where the system is going

That difference is where failures appear early.

Files

data/train.csv — training data

data/tester.csv — evaluation data

scorer.py — canonical evaluation script

baseline_heuristic.py — dataset-specific reference model

README.md — dataset card

Evaluation

Primary metric:

recall_trajectory_deterioration_detection

Secondary metric:

false_stable_trajectory_rate

Standard metrics are also reported:

accuracy

precision

recall

f1

The scorer supports binary predictions or score-based predictions.

License

MIT

Structural Note

Clarus datasets are structural instruments.

They are designed to expose instability geometry, not just predict isolated outcomes.

This v0.2 repo adds directional state movement so the dataset can separate static staffing pressure from active deterioration in the staffing–acuity relationship.

Production Deployment

This dataset can be used in:

staffing strain studies

acuity escalation monitoring

hospital operations simulations

care coordination benchmarking

model benchmarking for trajectory-aware clinical staffing reasoning

It is suitable for research and prototyping.

It is not a substitute for live clinical judgment.

Enterprise & Research Collaboration

Clarus builds datasets for:

instability detection

trajectory tracking

intervention reasoning

These structures are not domain-bound.

They apply wherever systems move toward or away from failure.

Applicable domains include:

healthcare systems

financial markets

energy infrastructure

logistics networks

artificial intelligence systems

manufacturing systems

supply chains

climate systems

Any environment where:

capacity and demand interact

delays and coupling exist

trajectory determines outcome

This dataset is one instance of a general stability framework.