version: "1.0.0" name: equiflow description: Generate equity-focused cohort selection flow diagrams. Tracks demographic, socioeconomic, and outcome variables at each exclusion step and calculates SMD to detect selection bias. Use for cohort construction, CONSORT-style diagrams, or bias detection in clinical ML/research. tier: expert category: clinical kind: methodology

EquiFlow - Equity-Focused Cohort Flow Diagrams

Visualize and quantify selection bias in clinical ML/research cohorts. Based on Ellen et al. (2024) J Biomed Inform.

When to Use This Skill

• Building patient cohorts from MIMIC-IV or eICU
• Documenting inclusion/exclusion criteria with CONSORT-style diagrams
• Detecting disproportionate exclusion of vulnerable groups
• Quantifying selection bias via Standardized Mean Difference (SMD)

Default Equity Variables

When no variables are specified, CohortFlow automatically tracks:

SMD Interpretation

See references/smd_interpretation.md for detailed guidance.

Critical Implementation Notes

1. Auto-detection: CohortFlow scans DataFrame columns for known aliases. Override with use_defaults=False if you want full control.

1. SMD > 0.2 is the default threshold for flagging potential bias. This follows established covariate balance literature.

1. Missing data: Exclusion steps that remove patients with missing values can introduce systematic bias. Always check SMD after such steps.

Example Queries

Full workflow with MIMIC-IV

from cohort_flow import CohortFlow
# Step 1: Query MIMIC-IV
query = """
SELECT
    p.subject_id, p.gender, p.anchor_age,
    a.race, a.insurance, a.language, a.marital_status,
    a.hospital_expire_flag,
    i.los
FROM mimiciv_hosp.patients p
JOIN mimiciv_hosp.admissions a USING (subject_id)
JOIN mimiciv_icu.icustays i ON a.hadm_id = i.hadm_id
"""
df = execute_query(query)
# Step 2: Build cohort with equity tracking
cf = CohortFlow(df)  # Auto-detects equity variables
cf.exclude(df['anchor_age'] >= 18, "Age < 18", "Adults")
cf.exclude(df['los'] >= 24, "ICU < 24h", "ICU >= 24h")
cf.exclude(df['anchor_age'] <= 90, "Age > 90", "Age 18-90")
# Step 3: Check for bias
print(cf.check_bias())  # Flags variables with SMD > 0.2
# Step 4: Generate diagram
cf.plot("sepsis_cohort_flow")

Minimal example

cf = CohortFlow(df)
cf.exclude(df['anchor_age'] >= 18, "Age < 18", "Adults")
cf.view_drifts()  # SMD values after exclusion

Dependencies

pip install equiflow

References

• Ellen JG, et al. "Participant flow diagrams for health equity in AI." J Biomed Inform. 2024;152:104631.