INTEGRATION OF EXPERT SYSTEMS AND MACHINE LEARNING ALGORITHMS IN THE CREATION OF A KNOWLEDGE BASE FOR DECISION-MAKING SYSTEMS IN PRIMARY CARE
Keywords:
primary care, clinical decision-making system, knowledge base, expert system, machine learning, artificial intelligence, Medical Informatics.Abstract
This article discusses the mutual integration of expert systems and machine learning algorithms in the formation of a knowledge base in order to improve the effectiveness of clinical decision-making systems (CDSS) in primary care. While traditional expert systems in medical decision-making are useful in formalizing knowledge and making rule-based recommendations, the large-scale, complex, and dynamic information emerging in modern medical practice shows the limitations of this approach. For this reason, systems used in conjunction with machine learning algorithms significantly increase medical decision accuracy, flexibility, and real-time analysis capability. The article covers the structuring of the knowledge base, algorithmic analysis, experimental results based on real clinical cases, and the benefits of this approach on a scientific basis.
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