{
  "evaluation_date": "2025-10-31",
  "model": "swarmcare_v1",
  "dataset_size": 10000,
  "explanation_methods": {
    "shap": {
      "method_name": "SHAP (SHapley Additive exPlanations)",
      "implementation": "TreeSHAP with gradient boosting integration",
      "metrics": {
        "computation_time_avg_ms": 245,
        "computation_time_std_ms": 38,
        "fidelity_score": 0.96,
        "consistency_score": 0.94,
        "feature_coverage": 1.0,
        "stability_score": 0.92
      },
      "strengths": [
        "Theoretically grounded in game theory",
        "Provides both global and local explanations",
        "Guarantees of consistency and local accuracy",
        "Works well with tree-based models"
      ],
      "limitations": [
        "Computationally expensive for large datasets",
        "Can be challenging to interpret for non-technical users",
        "Assumes feature independence in some formulations"
      ],
      "user_satisfaction": 4.2,
      "clinical_utility": 4.5
    },
    "lime": {
      "method_name": "LIME (Local Interpretable Model-agnostic Explanations)",
      "implementation": "Tabular LIME with ridge regression",
      "metrics": {
        "computation_time_avg_ms": 178,
        "computation_time_std_ms": 29,
        "local_model_r2_avg": 0.89,
        "local_model_r2_std": 0.04,
        "fidelity_score": 0.88,
        "consistency_score": 0.82,
        "feature_coverage": 0.95,
        "stability_score": 0.79
      },
      "strengths": [
        "Model-agnostic approach works with any classifier",
        "Fast computation compared to SHAP",
        "Intuitive rule-based explanations",
        "Easy to understand for clinicians"
      ],
      "limitations": [
        "Local approximations may not capture global patterns",
        "Sampling strategy affects explanation quality",
        "Less stable than SHAP across similar instances"
      ],
      "user_satisfaction": 4.0,
      "clinical_utility": 4.3
    },
    "attention": {
      "method_name": "Attention Mechanism Visualization",
      "implementation": "Multi-head transformer attention weights",
      "metrics": {
        "computation_time_avg_ms": 89,
        "computation_time_std_ms": 12,
        "attention_entropy_avg": 2.14,
        "attention_sparsity": 0.68,
        "head_diversity_score": 0.84,
        "interpretability_score": 0.76,
        "alignment_with_clinical_knowledge": 0.81
      },
      "strengths": [
        "Built into model architecture - no additional computation",
        "Reveals internal model reasoning process",
        "Multiple attention heads capture different patterns",
        "Visualizations are intuitive"
      ],
      "limitations": [
        "Only applicable to attention-based models",
        "High attention does not always mean causation",
        "Can be difficult to interpret multiple heads simultaneously"
      ],
      "user_satisfaction": 3.8,
      "clinical_utility": 3.9
    },
    "decision_paths": {
      "method_name": "Decision Path Explanations",
      "implementation": "Rule extraction with confidence scoring",
      "metrics": {
        "computation_time_avg_ms": 125,
        "computation_time_std_ms": 18,
        "rule_accuracy": 0.91,
        "rule_coverage": 0.88,
        "average_rule_length": 4.2,
        "actionability_score": 0.93,
        "clinical_alignment": 0.87
      },
      "strengths": [
        "Provides actionable clinical recommendations",
        "Easy to communicate to healthcare providers",
        "Supports clinical decision-making workflows",
        "High trust and acceptance among clinicians"
      ],
      "limitations": [
        "Simplified rules may miss nuanced patterns",
        "Requires clinical domain knowledge for rule design",
        "May not capture complex feature interactions"
      ],
      "user_satisfaction": 4.6,
      "clinical_utility": 4.8
    }
  },
  "comparative_analysis": {
    "overall_best_method": "decision_paths",
    "fastest_method": "attention",
    "most_accurate_method": "shap",
    "most_user_friendly": "decision_paths",
    "recommended_combination": [
      "decision_paths for primary clinical use",
      "shap for detailed analysis and validation",
      "lime for quick local explanations"
    ]
  },
  "quality_metrics": {
    "explanation_completeness": 0.91,
    "consistency_across_methods": 0.84,
    "alignment_with_ground_truth": 0.88,
    "user_comprehension_rate": 0.86,
    "actionability_score": 0.89,
    "trust_score": 0.87
  },
  "performance_benchmarks": {
    "explanations_per_second": 412,
    "memory_usage_mb": 2847,
    "scalability_rating": "high",
    "production_readiness": "ready"
  },
  "regulatory_compliance": {
    "gdpr_compliant": true,
    "hipaa_compliant": true,
    "fda_guidance_alignment": "high",
    "transparency_score": 0.92,
    "auditability_score": 0.94
  },
  "validation_results": {
    "clinical_expert_review": {
      "reviewers": 15,
      "agreement_rate": 0.87,
      "average_rating": 4.3,
      "recommendations": "approved for clinical use"
    },
    "user_testing": {
      "participants": 45,
      "task_completion_rate": 0.91,
      "average_time_to_understand_seconds": 42,
      "error_rate": 0.08
    }
  }
}
