Modern clinical guidelines play a crucial role in medical practice; however, their textual format often hinders machine processing and integration with decision support systems. This article presents a method for transforming traditional clinical guidelines into a digital format that ensures machine-executable representation, demonstrated using cardiovascular disease guidelines as an example. The proposed method organizes clinical knowledge into interconnected ontological knowledge graphs, enabling seamless integration with data analysis algorithms and knowledge bases. Converting guidelines into ontological knowledge graphs makes each clinical statement computationally traceable, dynamically adjustable, and adaptable to individual patient contexts. This solution lays the foundation for transitioning to machine-executable medical knowledge – a critical step toward personalized care.