Abductive Neuro-Symbolic Engine

Executive Abstract

Modern Large Language Models (LLMs) excel at probabilistic syntactic parsing and fuzzy entity extraction from uncurated clinical narratives, yet fail systematically when tasked with rigid, safety-critical deduction. Hallucinations and subtle logical inconsistencies in medical polypharmacy regimens present unacceptable patient risks.

The Abductive Neuro-Symbolic Engine resolves this fundamental tension by establishing a two-tier hybrid architecture:

1. Probabilistic Perception Layer: An LLM agent parses natural language electronic health records (EHR) into strongly-typed Pydantic assertions.
2. Deterministic Verification Layer: Assertions are compiled into First-Order Logic (FOL) formulas evaluated by Microsoft Z3 SMT Solver to mathematically verify that proposed interventions satisfy all axiomatic clinical guidelines without contradiction.

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Methodology & Architecture

The system decouples fuzzy semantic comprehension from mathematical theorem proving. When a clinician enters an unstructured patient profile, the pipeline progresses deterministically:

[Unstructured Narrative] ──> [LLM Semantic Extractor] ──> [Pydantic Structural Model] ──> [Z3 SMT Solver] ──> [SAT / UNSAT Proof]

1. Semantic Extraction & Strict Typing

The model constrains LLM output into typed ASTs validated via Pydantic. Any schema failure triggers an immediate rejection before reaching the constraint engine:

from pydantic import BaseModel, Field
from typing import List, Optional

class PatientBiomarkers(BaseModel):
    patient_id: str
    egfr_ml_min: float = Field(..., ge=0.0, le=200.0)
    serum_potassium_meq_l: float = Field(..., ge=1.0, le=10.0)
    systolic_bp_mmhg: int = Field(..., ge=50, le=260)
    active_prescriptions: List[str]
    diagnosed_comorbidities: List[str]

2. SMT Theory Generation & Solvability

The validated biomarkers and intended clinical interventions are converted into first-order clauses. The Z3 SMT engine evaluates satisfiability against axiomatic medical contraindications:

from z3 import Solver, Real, Bool, And, Implies, sat, unsat

def verify_clinical_safety(egfr_val: float, potassium_val: float, prescribing_spironolactone: bool):
    s = Solver()
    
    # Mathematical variables
    egfr = Real('egfr')
    k_serum = Real('k_serum')
    rx_spironolactone = Bool('rx_spironolactone')
    hyperkalemia_risk = Bool('hyperkalemia_risk')
    
    # Ground truth clinical state
    s.add(egfr == egfr_val)
    s.add(k_serum == potassium_val)
    s.add(rx_spironolactone == prescribing_spironolactone)
    
    # Axiom: KDIGO / ACC Guideline for Aldosterone Antagonists
    # Contraindicated if eGFR < 30 mL/min OR Serum Potassium > 5.0 mEq/L
    contraindication_rule = Implies(
        rx_spironolactone,
        And(egfr >= 30.0, k_serum <= 5.0)
    )
    s.add(contraindication_rule)
    
    result = s.check()
    if result == sat:
        return {"status": "VERIFIED", "model": s.model()}
    else:
        return {"status": "UNSAT_CONTRADICTION", "reason": "Severe Hyperkalemia / Renal Risk"}

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Proof & Verification Results

By treating clinical safety as an automated theorem proving problem rather than next-token probability, the engine guarantees:

• Zero False Passes: Unsound drug combinations that violate guideline constraints yield UNSAT with 100% mathematical certainty.
• Traceable Counterexamples: When a regimen fails, Z3 produces unsat-core diagnostics identifying the exact conflicting premises (e.g., eGFR threshold vs. ACE-inhibitor combination).
• CLI-First Tooling: Designed as a lightweight, headless UNIX CLI node operating locally without external cloud telemetry dependencies.