Representing health, disorder and their transitions by digraphs

Area of Science:

• Computational biology
• Medical informatics
• Graph theory

Background:

• Clinical decision making (CDM) traditionally relies on recognizing patterns of disease.
• Modeling the complex interplay between etiology and human physiology presents a significant challenge.

Purpose of the Study:

• To formalize clinical decision making (CDM) using a novel directed graph (digraph) approach.
• To represent etiological factors and the human body within a unified digraph framework.
• To model transitions between healthy and disordered states.

Main Methods:

• Representing etiology and the human body using a single directed graph (digraph).
• Utilizing standard digraph operators (change, add, delete, contract) for modeling state transitions.
• Defining digraph nodes with a composite structure: .

Main Results:

• Paths in the etiological subdigraph model epidemiological spread.
• Virulent paths represent the entry and damage of etiological agents.
• Pathogenetic mechanisms define internal pathways within the body, distinct from etiology.
• Clinical decision making (CDM) is achieved by differentiating between healthy and disordered digraphs.

Conclusions:

• The study presents a novel and powerful graph-based approach to clinical decision making (CDM).
• Formalizing health and disorder states using digraphs offers a new paradigm for medical diagnosis and understanding disease progression.