The article explores the significance and methodologies of constructing robust knowledge bases for Decision Support Systems (DSS) within primary healthcare settings. DSS play a pivotal role in assisting healthcare professionals by providing data-driven insights and recommendations based on patient data, clinical guidelines, and medical research. This article discusses various models, including expert systems, ontological models, case-based reasoning (CBR), and machine learning techniques, that are used to construct these knowledge bases. Additionally, it highlights key algorithms such as decision trees, Bayesian networks, neural networks, and natural language processing (NLP) that are crucial for processing, analyzing, and retrieving relevant knowledge from vast datasets. The paper also addresses the importance of regularly updating knowledge bases to maintain their accuracy and relevance. By incorporating these advanced computational models and algorithms, primary healthcare systems can enhance decision-making, improve diagnostic accuracy, and provide personalized, efficient care to patients. The article emphasizes the potential of DSS to improve overall healthcare outcomes through intelligent and evidence-based recommendations.

decision support systems (DSS), knowledge base, primary healthcare, expert systems, case-based reasoning (CBR), algorithms, clinical decision-making, healthcare data analysis, predictive models, natural language processing (NLP), healthcare informatics.

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