Clinical diagnosis support system based on case based fuzzy cognitive maps and semantic web

Area of Science:

• Medical Informatics
• Artificial Intelligence in Medicine
• Clinical Decision Support Systems

Background:

• Improper diagnostic test utilization significantly impacts patient health outcomes and healthcare costs.
• Clinical Decision Support Systems aim to optimize diagnostic test use in clinical practice.
• Computerized medical reasoning requires integrating existing knowledge, physician experience, and new information.

Purpose of the Study:

• To propose Case Based Fuzzy Cognitive Maps (CBFCM) as an advancement over Fuzzy Cognitive Maps.
• To enhance diagnostic decision-making by incorporating case-based fuzzy rules.
• To develop a framework for utilizing patient data and formalizing knowledge from clinical guidelines.

Main Methods:

• Developed a framework for patient data interaction and knowledge formalization from Urinary Tract Infection guidelines.
• Utilized Semantic Web tools to implement cognitive approaches for guideline integration.
• Introduced case-based fuzzy rules within Fuzzy Cognitive Maps to create CBFCM.

Main Results:

• CBFCM allows for a more comprehensive representation of medical knowledge.
• The framework facilitates the integration of clinical guidelines and physician experiences.
• Demonstrated the feasibility of using cognitive approaches with Semantic Web tools for guideline implementation.

Conclusions:

• Case Based Fuzzy Cognitive Maps offer an improved method for diagnostic decision support.
• The proposed framework enables knowledge sharing and reuse from guidelines and physician experience.
• This approach simplifies the maintenance of clinical decision support systems.