A Serverless Pharmacogenomic Risk Dashboard: Translating Ensemble Models and Model-Based Scenario Rules to Clinical Decision Support.

The "last mile" problem in healthcare AI-translating high-performance models into accessible, privacy-preserving point-of-care tools-remains unsolved for pharmacogenomic (PGx) risk assessment. No existing platform integrates opioid and polypharmacy risk scoring, model-based scenario analysis, and CPIC-based PGx patient cards within a single privacy-first serverless architecture. We designed, developed, and implemented the PGx Risk Dashboard: a serverless system combining an S3-hosted static frontend with AWS Lambda containerized backends. Fifty-six bin-level ensembles (2 cohorts × 7 age bands × 4 density bins), each comprising CatBoost/XGBoost/XGBoost-RF base learners, are packaged within an AWS Lambda container, with aggregate fallback artifacts used for sparse cells. Partial-input imputation using training-set medians handles real-world data sparsity; the PGx Patient Card executes stateless CPIC lookups for 573 deterministic gene-drug logic-verification cases without storing any PII. Lambda cold-start latency was mean 2100 ms (SD 250 ms); warm inference latency was mean 6 ms (SD 1 ms), meeting the sub-100 ms target. Prediction stability was maintained under sparse inputs (≤ 5 features provided): mean |Δp̂| = 0.10 vs. full-feature baseline. The PGx Risk Dashboard demonstrates technical feasibility for low-latency, privacy-first PGx risk decision support without EHR integration. We maintain the association-versus-causation distinction to support safer clinical use, avoid overstating treatment-effect evidence from observational outputs, and preserve a clear path to prospective causal-effect validation as next steps.