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A clinical decision-support system for dengue based on fuzzy cognitive maps
William Hoyos1,2, Jose Aguilar3,4,5, Mauricio Toro2
1Grupo de Investigaciones Microbiológicas y Biomédicas de Córdoba, Universidad de Córdoba, Carrera 6 No 77-305, Montería, Colombia.
Health Care Management Science
|August 15, 2022
Summary
This study introduces a novel clinical decision-support system for dengue diagnosis. The explainable AI model accurately classifies dengue severity using fuzzy cognitive maps, aiding prompt medical intervention.
Area of Science:
- Medical Informatics
- Infectious Diseases
- Artificial Intelligence
Background:
- Dengue is a widespread viral infection in tropical/subtropical areas.
- Prompt and accurate diagnosis is crucial to reduce high fatality rates.
- Existing diagnostic methods can be improved with advanced decision-support tools.
Purpose of the Study:
- To develop and evaluate a clinical decision-support system for dengue diagnosis.
- To classify dengue clinical presentation by severity.
- To analyze the causal relationships of clinical and laboratory variables in dengue.
Main Methods:
- A fuzzy cognitive map-based system was developed using dengue signs, symptoms, and lab tests.
- The system classifies clinical pictures based on severity.
- Model performance was evaluated on dengue patient datasets.
Main Results:
- The developed model achieved 89.4% accuracy in classifying dengue severity.
- The system demonstrated an ability to evaluate the behavior of clinical and laboratory variables.
- The method is explainable, providing insights into diagnostic reasoning.
Conclusions:
- The fuzzy cognitive map model serves as an effective diagnostic aid for dengue.
- The system can assist medical professionals in clinical settings for better patient management.
- This approach enhances dengue diagnosis by providing accurate severity classification and variable analysis.
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