By name: "KSB-D10-K0036" description: "Machine Learning Risk Stratification: Patient segmentation algorithms, treatment response prediction, adverse event forecasting, personali..." version: "1.0.0" domain: "D10" domain_name: "Benefit-Risk Assessment" type: "Knowledge" proficiency_level: "L4" bloom_level: "evaluate" triggers: - "explain machine learning risk stratification" - "what is machine learning risk stratification" - "evaluate machine learning risk stratification" epa_mapping: "EPA-03, EPA-05, EPA-08" cpa_mapping: "CPA-02, CPA-03" regulatory_refs: "CIOMS-X, ICH-E2E"
KSB-D10-K0036: Machine Learning Risk Stratification
Overview
Domain: D10 - Benefit-Risk Assessment Type: Knowledge Proficiency Level: L4 (Proficient - Independent practice) Bloom Level: Evaluate
Description
Patient segmentation algorithms, treatment response prediction, adverse event forecasting, personalized risk assessment
Context
- Major Section: Benefit-Risk Assessment Theoretical Foundation and Decision Science
- Section: AI-Enhanced Quantitative Assessment and Decision Optimization
EPA Mapping
- EPA-03:3006-3008
- EPA-05:3011-3012
- EPA-08:3018-3019
CPA Pathway
- CPA-02, CPA-03
Regulatory References
- CIOMS-X
- ICH-E2E
Instructions
When this skill is activated, Claude should:
- Demonstrate L4 proficiency in machine learning risk stratification
- Apply evaluate level cognitive skills to evaluate the topic
- Reference relevant regulatory guidance (CIOMS-X, ICH-E2E)
- Connect to related EPAs: EPA-03, EPA-05, EPA-08
Key Competencies
- Patient segmentation algorithms, treatment response prediction, adverse event forecasting, personalized risk assessment
Assessment Criteria
- Can evaluate core concepts independently
- Demonstrates understanding of regulatory context
- Applies knowledge appropriately to PV scenarios
Related Skills
- Other D10 skills in AI-Enhanced Quantitative Assessment and Decision Optimization
- Cross-domain integrations per DAG architecture
Generated from PV KSB Framework v1.0 | 2025-12-31