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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Constructing Clinical Decision Support Systems for Adverse Drug Event Prevention: A Knowledge-based Approach
Vassilis Koutkias1, Vassilis Kilintzis, George Stalidis
1Aristotle University, Medical School, Lab of Medical Informatics, Thessaloniki, Greece;
AMIA ... Annual Symposium Proceedings. AMIA Symposium
|February 25, 2011
Summary
This study introduces a knowledge-based framework to prevent Adverse Drug Events (ADEs). It uses a Knowledge Base (KB) and inference engine to offer clinical decision support, aiding healthcare professionals and patients in identifying preventable ADEs.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Healthcare
- Patient Safety
Background:
- Adverse Drug Events (ADEs) pose a significant risk in healthcare.
- Effective management and utilization of knowledge are crucial for ADE prevention.
- Existing systems may lack comprehensive decision support for preventable ADEs.
Purpose of the Study:
- To propose a knowledge-based framework for managing and utilizing knowledge on ADE prevention.
- To develop a Knowledge-Based System (KBS) for clinical decision support.
- To provide alerts and recommendations for preventable ADEs to healthcare professionals and patients.
Main Methods:
- Development of a framework centered around a Knowledge Base (KB) with rule-based knowledge sources.
- Implementation of inference and query mechanisms for decision support.
- Construction of the KBS within the EU-funded PSIP project.
Main Results:
- Presentation of the framework's foundations, knowledge model, and KB structure.
- Details on KB population and KBS implementation progress.
- Verification results of the KBS in decision support operations.
Conclusions:
- The proposed framework provides a robust foundation for ADE prevention through a KBS.
- The system offers valuable decision support services for identifying and preventing ADEs.
- Ongoing development and verification demonstrate the KBS's potential in clinical practice.
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