About This Architecture

Pharmacogenomics risk prediction workflow processes VCF genomic data through AI-powered drug safety analysis. User submits genetic variants via Web App UI, triggering VCF Parser to extract genomic markers for AI Risk Engine evaluation. AI Risk Engine coordinates LLM Module for natural language explanations and CPIC Module for clinical pharmacogenomics guidelines, returning color-coded risk scores (green=safe, yellow=adjust dosage, red=toxic contraindication) to Results Dashboard. This sequence diagram maps the complete data flow from patient genetic input through multi-model risk assessment to actionable clinical decision support. Fork this template on Diagrams.so to customize risk thresholds, add new genomic databases, or integrate with EHR systems for precision medicine workflows.

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How do you architect an AI system that predicts drug toxicity risk from patient genomic data using pharmacogenomics guidelines?

This sequence diagram shows a five-phase workflow: VCF Parser extracts genetic variants, AI Risk Engine coordinates LLM Module for explanations and CPIC Module for clinical guidelines, then Results Dashboard displays color-coded safety predictions (green/yellow/red) for precision dosing decisions.