Risk Stratification

from modeling.risk import stratify_risk

# Get predicted probabilities from your model
proba = risk_model.predict_proba(X_test)[:, 1]

# Stratify into risk bands
risk_df = stratify_risk(
    probabilities=proba,
    low_threshold=0.35,
    high_threshold=0.7
)

   risk_probability  risk_band
0              0.23        low
1              0.58     medium
2              0.85       high
3              0.42     medium
4              0.12        low

risk_band = pd.Series("medium", index=probabilities.index, dtype="object")
risk_band.loc[probabilities < low_threshold] = "low"
risk_band.loc[probabilities >= high_threshold] = "high"

from modeling.risk import summarize_risk_bands

summary = summarize_risk_bands(risk_df)

{
    "low_prevalence": 0.42,
    "medium_prevalence": 0.38,
    "high_prevalence": 0.20
}

# High-risk patients need immediate attention
high_risk_patients = risk_df[risk_df["risk_band"] == "high"]
print(f"Urgent cases: {len(high_risk_patients)}")

# Assign ICU beds to high-risk patients
for risk_band, group in risk_df.groupby("risk_band"):
    if risk_band == "high":
        allocate_icu_beds(group.index)
    elif risk_band == "medium":
        allocate_standard_beds(group.index)
    else:
        schedule_outpatient_followup(group.index)

# More conservative during high demand
risk_df = stratify_risk(
    probabilities=proba,
    low_threshold=0.25,  # Lower bar for medium risk
    high_threshold=0.60   # Lower bar for high risk
)

from modeling.predictive import train_predictive_models, SimpleLogisticModel
from modeling.risk import stratify_risk, summarize_risk_bands

# Train risk prediction model
feature_cols = ["age", "pain_level", "bmi", "wait_time_min"]
artifacts = train_predictive_models(
    df=patient_data,
    feature_cols=feature_cols,
    risk_target="diagnosis",
    outcome_target="readmitted"
)

# Generate risk probabilities for new patients
new_patients = load_new_admissions()
X_new = prepare_features(new_patients, feature_cols)
proba = artifacts.risk_model.predict_proba(X_new)[:, 1]

# Stratify and summarize
risk_df = stratify_risk(proba)
summary = summarize_risk_bands(risk_df)

print(f"High-risk patients: {summary['high_prevalence']:.1%}")
print(f"Medium-risk patients: {summary['medium_prevalence']:.1%}")
print(f"Low-risk patients: {summary['low_prevalence']:.1%}")