ML Model Validation & Regulatory Readiness

Overview

A model that influences care is a regulated, risk-bearing asset. This skill wraps the engineering of healthcare-predictive-modeling and explainable-ml-healthcare in the validation, documentation, and monitoring that governance and regulators expect turning a trained model into an auditable, maintainable production system aligned with GMLP, IEC 62304, and ISO 14971.

When to Use This Skill

• Promoting a clinical/operational model from notebook to documented production.
• Preparing model documentation for internal governance, audit, or regulatory submission.
• Defining acceptance criteria and a validation protocol before deployment.
• Standing up post-deployment monitoring for drift and performance decay.

Components

1. Intended use & risk intended-use statement, target population, role in the

clinical workflow; ISO 14971 risk analysis of failure modes and mitigations.

1. Validation protocol pre-specified datasets (internal + external/temporal),

acceptance thresholds (overall and per subgroup), and the analyses to run written before seeing test results.

1. Performance validation discrimination, calibration, and decision-curve/net-

benefit on held-out and external data; subgroup fairness against thresholds.

1. Robustness sensitivity to missingness, input shifts, and edge cases; stress tests.
2. Documentation a complete model card + datasheet: data lineage, training,

metrics, limitations, intended use, and explainability summary.

1. Lifecycle (IEC 62304) versioning of data/code/model, change control, and a

defined update/retraining procedure (and where applicable a Predetermined Change Control Plan).

1. Monitoring production dashboards for input drift (PSI/KS), prediction drift, and

live performance vs. acceptance thresholds, with alerts and a rollback plan.

Example

# Pre-specified validation gate (pass/fail against thresholds)
import numpy as np
from sklearn.metrics import roc_auc_score
from sklearn.calibration import calibration_curve

def validate(y, p, groups, min_auroc=0.75, max_calib_err=0.10, min_subgroup_auroc=0.70):
    report = {"auroc": roc_auc_score(y, p)}
    frac_pos, mean_pred = calibration_curve(y, p, n_bins=10)
    report["calibration_error"] = float(np.mean(np.abs(frac_pos - mean_pred)))
    report["subgroups"] = {g: roc_auc_score(y[idx], p[idx]) for g, idx in groups.items()}
    report["PASS"] = (report["auroc"] >= min_auroc
                      and report["calibration_error"] <= max_calib_err
                      and all(v >= min_subgroup_auroc for v in report["subgroups"].values()))
    return report

# Production drift monitor (Population Stability Index per feature)
def psi(expected, actual, bins=10):
    q = np.quantile(expected, np.linspace(0,1,bins+1))
    e = np.histogram(expected, q)[0]/len(expected) + 1e-6
    a = np.histogram(actual,   q)[0]/len(actual)  + 1e-6
    return float(np.sum((a-e)*np.log(a/e)))     # alert if > 0.25

Outputs

• validation_protocol.md (pre-registered) and validation_report.md (results vs. gate).
• model_card.md + datasheet.md governance-ready documentation.
• risk_analysis.md ISO 14971-style hazard/mitigation table.
• monitoring_plan.md + a drift/performance dashboard spec with alert thresholds.

Healthcare Context

Bridges data science and the medical-device quality world a natural complement to the repo's iso-13485-certification and hipaa-compliance skills. Encodes the non-negotiables for clinical AI: external/temporal validation, calibration, subgroup fairness, and live monitoring. This is documentation and process tooling, not legal advice; involve your regulatory/quality function for actual submissions.

References

• FDA/Health Canada/MHRA Good Machine Learning Practice (GMLP); FDA SaMD & AI/ML guidance.
• IEC 62304 (medical device software lifecycle); ISO 14971 (risk management).
• Sendak et al. (2020), Presenting Machine Learning Model Information to Clinical End Users (model facts labels).