A Neuro-Symbolic Knowledge Graph and Large Language Model Hybrid Architecture for Multi-Modality Mental Health Counseling

Abstract

Background: Depression and anxiety are managed largely between clinical visits, yet outpatient care lacks scalable, accountable mechanisms for between-visit support. Large language models converse fluently but fuse clinical reasoning with language generation in one opaque process, so they cannot reliably deliver evidence-based psychotherapy and typically operate outside clinician oversight. Objective: To evaluate C-Mind, a provider-supervised neuro-symbolic system in which a Clinical Knowledge Graph (KG) governs therapeutic decisions for a large language model across eight psychotherapy modalities. Methods: Two simulation regimes addressed eight pre-specified governance questions: a structural validation of KG routing against 117 guideline-anchored vignettes, and a governance battery using progressively disclosing LLM patient agents to evaluate decision traceability, repeatability, provenance auditability, adversarial crisis-detection robustness (277 probes), provider treatment-goal governance, and counselor technique adherence. Crisis detection was additionally validated externally against an independent, clinician-annotated corpus (CRADLE Bench). Results: The KG routed 116/117 vignettes (99.1%) to guideline-appropriate care and detected all 18 high-risk presentations, firing a therapy-suppressing hard halt on 16/18. Adversarial crisis-detection sensitivity was 96.7% and specificity 95.4% (277 probes); on external validation, the system detected 98.5% of 600 dialogues with ongoing suicidal ideation or self-harm at or before the annotator confirming turn. Decisions were 99.1% repeatable, 100% reconstructable per turn, and 100% provenance-auditable across all 354 KG nodes. Provider-set diagnosis, goals, and safety context governed behavior deterministically. Stripped of governance, the same model produced unsolicited clinical monologues on 100% of turns (vs 9% governed) and delivered diagnoses and medication advice the governed system never produced. Conclusions: A neuro-symbolic architecture achieves near-perfect guideline-appropriate routing with a governance profile, traceability, reproducibility, machine-traceable provenance, externally validated crisis detection, and deterministic provider control aligned with requirements for regulated clinical AI.