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Published on: September 26, 2018
Intelligent clinical decision support systems based on SNOMED CT.
Ewelina Ciolko1, Fletcher Lu, Amardeep Joshi
1Faculty of Health Science, University of Ontario Institute of Technology 2000 Simcoe Street North, Oshawa, Canada. ewelina.ciolko@uoit.ca
Summary
Physician diagnostic support systems need updated data. Analyzing artificial intelligence (AI) methods for SNOMED CT encoded documents, a Tree-Augmented Bayesian (TAN) network shows promise for timely diagnostic information, especially for emerging diseases.
Area of Science:
- Medical Informatics
- Artificial Intelligence
- Clinical Decision Support
Background:
- Current physician decision support systems often rely on outdated, static data.
- This limitation poses challenges in rapidly evolving medical situations like emerging diseases and epidemics.
Purpose of the Study:
- To comprehensively analyze artificial intelligence (AI) methods applicable to SNOMED CT encoded documents.
- To identify AI approaches that can enable timely updates for diagnostic decision support systems.
Main Methods:
- Review and high-level comparison of various AI methodologies.
- Evaluation of AI suitability for mining information from SNOMED CT encoded clinical documents.
Main Results:
- Artificial intelligence methods can be applied to SNOMED CT encoded documents for enhanced decision support.
- A Tree-Augmented Bayesian (TAN) network was identified as the most suitable AI method for this application.
Conclusions:
- Utilizing AI to mine SNOMED CT data can create dynamic, up-to-date diagnostic support systems.
- This approach is particularly valuable for managing information during emerging disease outbreaks and epidemics.
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