Clinical Decision Support System

for Thyroid Cancer Management

A new level of precision in clinical care, translating complex guidelines into actionable, evidence-based recommendations for healthcare professionals.

System Architecture: From Text to Treatment

Phase 1: Automated Rule Generation

(Offline Knowledge Building)

Unstructured Clinical Guidelines

Raw text from established medical sources.

LLM Rule Generation Engine

Phase 1: Validates variable relevance.
Phase 2: Expands to complex, multi-conditional rules.

Neo4j Knowledge Graph

The structured, machine-readable "brain" of the system, populated only with validated rules.

Phase 2: Real-Time Inference

(Patient-Specific Recommendations)

Clinician Input

A specific set of patient findings is provided to the system.

Hybrid Inference Engine

Logic Modules: For simple, rapid analysis.
Graph Queries: For complex, nuanced scenarios.

Actionable Recommendations

Provides a clear "second opinion" on treatments, risks, and follow-up tests.

Key Innovations & Expertise

Automated Knowledge Base Creation

Efficiently translates complex, unstructured text into a structured and queriable knowledge graph.

Hybrid AI Architecture

Combines the pattern-recognition of LLMs with the logical precision of a graph database for reliability and intelligence.

Dynamic & Evidence-Based

Reasons over a rich network of evidence, providing nuanced recommendations beyond static flowcharts.