Medical Diagnosis Expert System
A MeTTa-based implementation of a classical rule-based system for clinical inference, demonstrating backward chaining to derive diagnoses from patient symptoms.
Overview
A MeTTa-based implementation of a classical rule-based system for clinical inference, demonstrating backward chaining to derive diagnoses from patient symptoms.
Problem
Automating clinical diagnosis requires interpretable reasoning paths. This project explores how classical expert system logic can be implemented in modern neurosymbolic frameworks like MeTTa to provide a bridge between raw data perception and rule-based diagnostic inference.
Dataset
Medical diagnostic rubrics and symptom-disease correlation rules used for validating the system's reasoning traces.
Architecture
A MeTTa-based expert system that implements backward chaining over symbolic rules stored in the AtomSpace. The system processes patient symptoms to derive potential diagnoses with 100% traceable reasoning paths.
Training
Definition of diagnostic rules and symptom relationships within the MeTTa environment, focusing on deterministic inference.
Results
Developed a functional diagnostic engine that derives clinical conclusions from symptoms, providing a transparent and evidence-based alternative to black-box models.
Visualizations
The Medical-Diagnosis-Expert-System is a MeTTa-based implementation of a classical rule-based system. It demonstrates the power of backward chaining to derive diagnoses from patient symptoms, providing a baseline for hybrid neuro-symbolic systems where neural perception feeds into symbolic diagnostic rules.