Psycho-Symbolic Reasoning Integration with AIMDS

Medical Behavior Prediction & Analysis

Document Version: 1.0.0 Created: 2025-10-30 Status: Planning Phase Compliance: HIPAA, GDPR, FDA 21 CFR Part 11

Executive Summary

Integration plan for incorporating psycho-symbolic reasoning capabilities into AIMDS for medical behavior prediction, patient risk assessment, and clinical decision support while maintaining strict privacy, security, and regulatory compliance.

Key Objectives:

Predict patient behavioral patterns using symbolic reasoning
Detect early warning signs of mental health deterioration
Support clinical decision-making with explainable AI
Maintain HIPAA compliance and patient privacy
Provide real-time risk scoring for patient safety

1. Architecture Overview

1.1 Integration Points with AIMDS

┌─────────────────────────────────────────────────────────────┐
│                 Medical Psycho-Symbolic Layer                │
├─────────────────────────────────────────────────────────────┤
│                                                              │
│  ┌──────────────┐   ┌──────────────┐   ┌──────────────┐   │
│  │  Symbolic    │──▶│  Behavioral  │──▶│   Clinical   │   │
│  │  Reasoning   │   │  Prediction  │   │   Decision   │   │
│  │              │   │              │   │   Support    │   │
│  └──────────────┘   └──────────────┘   └──────────────┘   │
│         │                   │                   │           │
│         └───────────────────┼───────────────────┘           │
│                             ▼                                │
│              ┌──────────────────────────┐                   │
│              │   AIMDS Integration      │                   │
│              │   Layer (Secure PHI)     │                   │
│              └──────────────────────────┘                   │
│                             │                                │
└─────────────────────────────┼────────────────────────────────┘
                              ▼
┌─────────────────────────────────────────────────────────────┐
│                    AIMDS Core Platform                       │
├─────────────────────────────────────────────────────────────┤
│  Detection (<10ms)  │  Analysis (<100ms)  │  Response       │
│  - Pattern Match    │  - Behavioral       │  - Mitigation   │
│  - PII Sanitize     │  - Temporal Attract │  - Audit        │
│  - Anomaly Detect   │  - Policy Verify    │  - Meta-Learn   │
└─────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────┐
│              Midstream Platform Components                   │
├─────────────────────────────────────────────────────────────┤
│  AgentDB          │  Temporal Attractor  │  Strange Loop    │
│  (Vector Search)  │  (Chaos Analysis)    │  (Meta-Learning) │
└─────────────────────────────────────────────────────────────┘

1.2 Data Flow Architecture

Patient Data → PHI Sanitization → Symbolic Encoder → AIMDS Detection
                                                           │
                                                           ▼
Clinical Context → Feature Extraction → Behavioral Analysis (Temporal)
                                                           │
                                                           ▼
Historical Patterns → Vector Search (AgentDB) → Risk Prediction
                                                           │
                                                           ▼
Decision Rules → LTL Policy Verification → Clinical Recommendations
                                                           │
                                                           ▼
                                    Audit Trail → Compliance Logging

2. Psycho-Symbolic Reasoning Framework

2.1 Symbolic Representation

Mental State Encoding:

// Symbolic mental state representation
pub struct MentalState {
    // Cognitive dimensions (0.0-1.0 normalized)
    pub affect: f64,           // Emotional valence
    pub arousal: f64,          // Activation level
    pub cognition: f64,        // Cognitive clarity
    pub social: f64,           // Social engagement
    pub behavioral: f64,       // Behavioral activation

    // Temporal context
    pub timestamp: DateTime<Utc>,
    pub session_id: String,
    pub patient_id_hash: String, // Anonymized

    // Symbolic markers
    pub risk_factors: Vec<RiskSymbol>,
    pub protective_factors: Vec<ProtectiveSymbol>,
}

pub enum RiskSymbol {
    IsolationPattern,
    SleepDisruption,
    SubstanceUse,
    CognitiveImpairment,
    EmotionalVolatility,
    SelfHarmIndicators,
    MedicationNonCompliance,
}

pub enum ProtectiveSymbol {
    SocialSupport,
    CopingSkills,
    TreatmentEngagement,
    RoutineStability,
    InsightAwareness,
}

2.2 Reasoning Engine

Symbolic Inference Rules:

pub struct PsychoSymbolicReasoner {
    // Rule-based reasoning
    inference_rules: Vec<InferenceRule>,

    // Probabilistic reasoning
    bayesian_network: BayesianNet,

    // Temporal reasoning
    temporal_logic: LTLChecker,

    // Integration with AIMDS
    aimds_analyzer: BehavioralAnalyzer,
}

pub struct InferenceRule {
    // IF condition THEN conclusion
    pub condition: Vec<SymbolicPredicate>,
    pub conclusion: ClinicalHypothesis,
    pub confidence: f64,
    pub evidence_base: String, // Research citation
}

pub enum SymbolicPredicate {
    StateEquals(MentalDimension, f64),
    StateGreaterThan(MentalDimension, f64),
    TemporalPattern(TemporalPattern),
    RiskFactorPresent(RiskSymbol),
    ProtectiveFactorAbsent(ProtectiveSymbol),
}

2.3 Behavioral Prediction Model

Integration with AIMDS Temporal Attractors:

pub struct BehaviorPredictor {
    // AIMDS behavioral analyzer
    temporal_analyzer: BehavioralAnalyzer,

    // Psycho-symbolic state tracker
    state_tracker: MentalStateTracker,

    // Prediction model
    predictor: NeuralPredictor,

    // Risk assessment
    risk_calculator: ClinicalRiskCalculator,
}

impl BehaviorPredictor {
    pub async fn predict_trajectory(
        &self,
        patient_history: Vec<MentalState>,
        current_state: MentalState,
    ) -> Result<BehaviorPrediction, PredictionError> {
        // 1. Convert to temporal sequence for AIMDS
        let temporal_sequence = self.state_tracker
            .to_temporal_sequence(&patient_history);

        // 2. Analyze with AIMDS behavioral analyzer
        let anomaly_score = self.temporal_analyzer
            .analyze_behavior(&temporal_sequence)
            .await?;

        // 3. Apply psycho-symbolic reasoning
        let symbolic_analysis = self.state_tracker
            .analyze_symbolic_patterns(&current_state);

        // 4. Generate prediction
        let prediction = self.predictor.predict(
            &anomaly_score,
            &symbolic_analysis,
        ).await?;

        // 5. Calculate clinical risk
        let risk_score = self.risk_calculator
            .calculate_risk(&prediction);

        Ok(BehaviorPrediction {
            predicted_trajectory: prediction.trajectory,
            risk_level: risk_score.level,
            confidence: prediction.confidence,
            recommended_interventions: risk_score.interventions,
            explainability: symbolic_analysis.explanation,
        })
    }
}

3. Medical Use Cases

3.1 Mental Health Risk Assessment

Early Warning System:

Depression relapse prediction
Suicide risk assessment
Anxiety disorder progression
Psychosis early detection
Substance abuse relapse prediction

Implementation:

pub struct MentalHealthMonitor {
    predictor: BehaviorPredictor,
    risk_threshold: f64,
    alert_system: ClinicalAlertSystem,
}

impl MentalHealthMonitor {
    pub async fn monitor_patient(
        &self,
        patient_id_hash: String,
        assessment_data: AssessmentData,
    ) -> Result<RiskAssessment, MonitorError> {
        // Convert assessment to mental state
        let mental_state = MentalState::from_assessment(assessment_data);

        // Load patient history (anonymized)
        let history = self.load_patient_history(&patient_id_hash).await?;

        // Predict behavior trajectory
        let prediction = self.predictor
            .predict_trajectory(history, mental_state)
            .await?;

        // Check if intervention needed
        if prediction.risk_level >= self.risk_threshold {
            self.alert_system.send_alert(AlertType::HighRisk {
                patient_id_hash: patient_id_hash.clone(),
                risk_level: prediction.risk_level,
                recommended_actions: prediction.recommended_interventions,
                explanation: prediction.explainability,
            }).await?;
        }

        Ok(RiskAssessment {
            risk_score: prediction.risk_level,
            confidence: prediction.confidence,
            trajectory: prediction.predicted_trajectory,
            interventions: prediction.recommended_interventions,
            explanation: prediction.explainability,
        })
    }
}

3.2 Treatment Response Prediction

Predicting Treatment Efficacy:

pub struct TreatmentPredictor {
    behavior_model: BehaviorPredictor,
    treatment_db: TreatmentDatabase,
}

impl TreatmentPredictor {
    pub async fn predict_response(
        &self,
        patient_profile: PatientProfile,
        proposed_treatment: Treatment,
    ) -> Result<TreatmentPrediction, PredictionError> {
        // Find similar cases using AgentDB vector search
        let similar_cases = self.treatment_db
            .find_similar_cases(&patient_profile)
            .await?;

        // Analyze historical outcomes
        let outcomes = self.analyze_outcomes(&similar_cases);

        // Predict response using behavioral model
        let prediction = self.behavior_model
            .predict_treatment_response(
                &patient_profile,
                &proposed_treatment,
                &outcomes,
            )
            .await?;

        Ok(prediction)
    }
}

3.3 Medication Compliance Monitoring

Non-Compliance Detection:

pub struct ComplianceMonitor {
    pattern_detector: PatternDetector,
    aimds_detector: DetectionService,
}

impl ComplianceMonitor {
    pub async fn detect_non_compliance(
        &self,
        patient_data: PatientData,
    ) -> Result<ComplianceReport, MonitorError> {
        // Extract behavioral patterns
        let patterns = self.pattern_detector
            .extract_patterns(&patient_data);

        // Use AIMDS to detect anomalies
        let anomalies = self.aimds_detector
            .detect_behavioral_anomalies(&patterns)
            .await?;

        // Identify non-compliance markers
        let non_compliance_risk = self.calculate_risk(&anomalies);

        Ok(ComplianceReport {
            compliance_score: non_compliance_risk.score,
            detected_patterns: anomalies.patterns,
            recommended_interventions: non_compliance_risk.interventions,
        })
    }
}

4. Privacy & Security

4.1 HIPAA Compliance

PHI Protection:

pub struct PHISanitizer {
    aimds_sanitizer: PiiSanitizer, // Use AIMDS PII detection
    encryption: Aes256Gcm,
    anonymizer: PatientAnonymizer,
}

impl PHISanitizer {
    pub async fn sanitize_input(
        &self,
        clinical_data: ClinicalData,
    ) -> Result<SanitizedData, SanitizationError> {
        // 1. Remove PII using AIMDS
        let pii_removed = self.aimds_sanitizer
            .remove_pii(&clinical_data.text)
            .await?;

        // 2. Anonymize patient identifiers
        let anonymized = self.anonymizer
            .anonymize_patient_data(&clinical_data);

        // 3. Encrypt sensitive fields
        let encrypted = self.encryption
            .encrypt_sensitive_fields(&anonymized)?;

        Ok(SanitizedData {
            data: encrypted,
            pii_removed: pii_removed.removed_items,
            anonymization_map: anonymized.mapping_hash,
        })
    }
}

Audit Trail:

pub struct ComplianceAuditor {
    audit_log: AuditLogger,
    aimds_auditor: AuditLog, // Use AIMDS audit system
}

impl ComplianceAuditor {
    pub async fn log_access(
        &self,
        access_event: DataAccessEvent,
    ) -> Result<(), AuditError> {
        // Log to AIMDS audit system
        self.aimds_auditor.log_event(AuditEvent {
            timestamp: Utc::now(),
            event_type: "phi_access",
            user_id: access_event.user_id,
            action: access_event.action,
            resource: access_event.resource,
            context: json!({
                "purpose": access_event.purpose,
                "approval": access_event.approval_id,
            }),
        }).await?;

        // Additional medical-specific logging
        self.audit_log.log_phi_access(access_event).await?;

        Ok(())
    }
}

4.2 Data Anonymization

De-identification Strategy:

Replace patient IDs with cryptographic hashes
Remove direct identifiers (names, addresses, SSN)
Use AIMDS PII detection for automated scrubbing
Apply differential privacy for aggregate statistics
Implement k-anonymity for data sharing

4.3 Access Control

Role-Based Access:

pub enum MedicalRole {
    Physician,
    Psychiatrist,
    Psychologist,
    Nurse,
    SocialWorker,
    Researcher,
    DataAnalyst,
}

pub struct AccessController {
    rbac: RoleBasedAccessControl,
    aimds_policy: PolicyVerifier, // Use AIMDS policy verification
}

impl AccessController {
    pub async fn authorize_access(
        &self,
        user: User,
        resource: MedicalResource,
        action: Action,
    ) -> Result<bool, AuthError> {
        // Check RBAC permissions
        let rbac_allowed = self.rbac
            .check_permission(&user.role, &resource, &action)?;

        if !rbac_allowed {
            return Ok(false);
        }

        // Verify with AIMDS policy engine
        let policy_allowed = self.aimds_policy
            .verify_policy(&user, &resource, &action)
            .await?;

        Ok(policy_allowed)
    }
}

5. Implementation Roadmap

Phase 1: Foundation (Months 1-3)

Integrate AIMDS core libraries
Implement PHI sanitization layer
Build symbolic representation framework
Create mental state encoding system
Develop audit logging infrastructure

Phase 2: Reasoning Engine (Months 4-6)

Implement inference rule engine
Build Bayesian reasoning network
Integrate temporal logic checker
Create behavioral prediction model
Develop risk assessment algorithms

Phase 3: Clinical Integration (Months 7-9)

Build clinical decision support API
Implement treatment predictor
Create compliance monitoring system
Develop alert and notification system
Build clinician dashboard

Phase 4: Validation & Deployment (Months 10-12)

6. Technical Specifications

6.1 API Design

Patient Risk Assessment:

POST /api/v1/medical/assess-risk
Content-Type: application/json
Authorization: Bearer {token}

{
  "patient_id_hash": "sha256_hash",
  "assessment_data": {
    "mental_state": {
      "affect": 0.3,
      "arousal": 0.7,
      "cognition": 0.5,
      "social": 0.4,
      "behavioral": 0.6
    },
    "risk_factors": ["IsolationPattern", "SleepDisruption"],
    "protective_factors": ["TreatmentEngagement"],
    "clinical_context": {
      "diagnosis": "major_depressive_disorder",
      "medications": ["sertraline_100mg"],
      "last_assessment": "2025-10-15T10:30:00Z"
    }
  }
}

Response:
{
  "risk_assessment": {
    "risk_score": 0.72,
    "risk_level": "moderate_high",
    "confidence": 0.85,
    "predicted_trajectory": [...],
    "recommended_interventions": [
      {
        "type": "increase_monitoring",
        "priority": "high",
        "rationale": "Isolation pattern detected with declining affect"
      },
      {
        "type": "medication_review",
        "priority": "medium",
        "rationale": "Current treatment may need adjustment"
      }
    ],
    "explanation": {
      "symbolic_reasoning": "Patient shows isolation pattern...",
      "temporal_analysis": "Behavioral trajectory indicates...",
      "similar_cases": 127
    }
  },
  "audit_id": "audit_xyz789"
}

Treatment Response Prediction:

POST /api/v1/medical/predict-treatment
Content-Type: application/json

{
  "patient_profile": {
    "patient_id_hash": "sha256_hash",
    "demographics": {
      "age_range": "35-44",
      "gender": "female"
    },
    "clinical_history": {
      "diagnoses": ["major_depressive_disorder"],
      "prior_treatments": ["citalopram", "cbt"],
      "response_history": [...]
    },
    "current_state": {...}
  },
  "proposed_treatment": {
    "type": "medication",
    "medication": "venlafaxine",
    "dosage": "75mg",
    "adjunct_therapy": "mindfulness_based_cbt"
  }
}

Response:
{
  "prediction": {
    "response_probability": 0.68,
    "response_type": "partial_remission",
    "time_to_response": "6-8 weeks",
    "confidence": 0.73,
    "similar_cases": 243,
    "evidence_base": [
      {
        "study": "Meta-analysis PMID:12345678",
        "relevance": 0.85
      }
    ]
  }
}

6.2 Performance Requirements

Latency Targets:

Risk assessment: < 200ms (leveraging AIMDS <10ms detection + <100ms analysis)
Treatment prediction: < 500ms
Compliance monitoring: < 100ms
Real-time alert generation: < 50ms

Throughput:

Support 10,000+ concurrent patients
Process 1,000 assessments/second
Handle 100 real-time alert evaluations/second

Accuracy:

Risk prediction AUC > 0.85
Treatment response prediction AUC > 0.75
False positive rate < 10%
Sensitivity for high-risk cases > 90%

6.3 Data Storage

Vector Database (AgentDB):

pub struct PatientVectorStore {
    agentdb: AgentDBClient,
}

impl PatientVectorStore {
    pub async fn store_patient_embedding(
        &self,
        patient_id_hash: String,
        embedding: Vec<f64>,
        metadata: PatientMetadata,
    ) -> Result<(), StorageError> {
        self.agentdb.insert(
            collection: "patient_states",
            vector: embedding,
            metadata: json!({
                "patient_id_hash": patient_id_hash,
                "timestamp": Utc::now(),
                "diagnosis": metadata.diagnosis,
                "risk_level": metadata.risk_level,
            }),
        ).await
    }

    pub async fn find_similar_patients(
        &self,
        query_embedding: Vec<f64>,
        k: usize,
    ) -> Result<Vec<SimilarPatient>, StorageError> {
        // Use AgentDB's 150x faster HNSW search
        let results = self.agentdb.search(
            collection: "patient_states",
            query: query_embedding,
            top_k: k,
        ).await?;

        Ok(results)
    }
}

Time-Series Storage:

Patient behavioral sequences
Treatment response timelines
Medication compliance history
Clinical assessment longitudinal data

7. Ethical & Clinical Considerations

7.1 Explainability

Clinical Transparency:

All predictions must provide human-readable explanations
Symbolic reasoning traces must be accessible to clinicians
Confidence scores must be clearly communicated
Similar case references for evidence-based reasoning

7.2 Human-in-the-Loop

Clinician Oversight:

AI provides recommendations, not decisions
High-risk predictions require clinician review
Override mechanisms for clinical judgment
Feedback loops for model improvement

7.3 Bias Mitigation

Fairness Considerations:

Regular bias audits across demographic groups
Balanced training data representation
Fairness metrics monitoring (demographic parity, equal opportunity)
Transparent reporting of model limitations

7.4 Safety Mechanisms

Patient Safety:

Conservative thresholds for high-risk alerts
Escalation protocols for critical predictions
Failsafe mechanisms for system errors
Regular clinical validation

8. Validation & Testing

8.1 Clinical Validation

Validation Studies:

Retrospective validation on historical data
Prospective validation with clinical outcomes
Inter-rater reliability with clinician assessments
Sensitivity analysis for edge cases

8.2 Performance Benchmarks

Test Metrics:

Prediction accuracy (AUROC, AUPRC)
Calibration curves
Precision-recall at various thresholds
Latency measurements
Throughput stress testing

8.3 Compliance Testing

Regulatory Validation:

HIPAA Security Rule compliance
GDPR Article 22 (automated decision-making)
FDA 21 CFR Part 11 (if applicable)
State medical privacy laws

9. Deployment Architecture

9.1 Infrastructure

Cloud Deployment:

# Kubernetes deployment
apiVersion: apps/v1
kind: Deployment
metadata:
  name: psycho-symbolic-medical-api
spec:
  replicas: 3
  selector:
    matchLabels:
      app: medical-reasoning
  template:
    spec:
      containers:
      - name: api-server
        image: aimds/medical-reasoning:latest
        env:
        - name: AIMDS_ENDPOINT
          value: "http://aimds-service:3000"
        - name: AGENTDB_ENDPOINT
          value: "http://agentdb-service:8080"
        resources:
          requests:
            memory: "4Gi"
            cpu: "2"
          limits:
            memory: "8Gi"
            cpu: "4"
        livenessProbe:
          httpGet:
            path: /health
            port: 8080
          initialDelaySeconds: 30
          periodSeconds: 10

9.2 Monitoring

Observability Stack:

Prometheus metrics (via AIMDS metrics system)
Grafana dashboards for clinical metrics
ELK stack for audit log analysis
Alert Manager for critical notifications

9.3 Disaster Recovery

Business Continuity:

Multi-region deployment
Automated backups (encrypted)
Point-in-time recovery
Failover procedures
Data replication strategy

10. Future Enhancements

10.1 Advanced Features

Multi-modal data integration (text, speech, wearables)
Causal inference for treatment effects
Federated learning across institutions
Real-time EEG/physiological monitoring integration
Natural language understanding for clinical notes

10.2 Research Integration

Clinical trial matching
Personalized medicine protocols
Population health analytics
Epidemiological modeling

11. References

Academic Literature

DSM-5 diagnostic criteria encoding
ICD-10/11 classification systems
Clinical prediction rule development
Behavioral psychology research
Temporal dynamics in mental health

Technical Standards

HL7 FHIR for healthcare interoperability
SNOMED CT clinical terminology
LOINC for laboratory observations
IEEE 11073 for medical device communication

Regulatory Guidance

FDA guidance on clinical decision support
HIPAA Security Rule
GDPR Article 9 (health data processing)
ISO 27001 information security

Appendix A: Symbolic Encoding Examples

Depression State Encoding

MentalState {
    affect: 0.25,              // Low positive affect
    arousal: 0.35,             // Low energy
    cognition: 0.40,           // Impaired concentration
    social: 0.30,              // Social withdrawal
    behavioral: 0.35,          // Reduced activity
    risk_factors: vec![
        RiskSymbol::IsolationPattern,
        RiskSymbol::SleepDisruption,
        RiskSymbol::CognitiveImpairment,
    ],
    protective_factors: vec![
        ProtectiveSymbol::TreatmentEngagement,
    ],
}

Anxiety State Encoding

MentalState {
    affect: 0.40,              // Negative valence
    arousal: 0.85,             // High arousal
    cognition: 0.55,           // Racing thoughts
    social: 0.50,              // Avoidance
    behavioral: 0.65,          // Restlessness
    risk_factors: vec![
        RiskSymbol::SleepDisruption,
        RiskSymbol::SubstanceUse,
    ],
    protective_factors: vec![
        ProtectiveSymbol::CopingSkills,
        ProtectiveSymbol::SocialSupport,
    ],
}

Appendix B: Integration Code Templates

See implementation examples in:

/plans/examples/risk-assessment.rs
/plans/examples/treatment-prediction.rs
/plans/examples/compliance-monitoring.rs

Document Control:

Version: 1.0.0
Last Updated: 2025-10-30
Next Review: 2025-11-30
Owner: Medical AI Integration Team
Approvers: Clinical Lead, HIPAA Officer, Technical Lead

ruvnet/Psycho-Symbolic.md