MATHEMATICAL MODELS AND ALGORITHMS FOR CLINICAL DECISION SUPPORT IN HEMATOLOGY BASED ON BLOOD GASOMETRY TECHNOLOGY

Abstract

This study presents the mathematical and algorithmic framework of a clinical decision support system for hematological patients based on blood gas analysis. A computational model integrating gas exchange equations and acid–base balance relationships, including the Henderson–Hasselbalch equation, anion gap assessment, and compensatory mechanisms, was developed. The proposed algorithm formalizes the interpretation of рН, , and tissue oxygenation parameters, enabling differentiation between metabolic and respiratory disorders. The results demonstrate improved objectivity and reproducibility of diagnostic evaluation and highlight the feasibility of integrating the model into clinical information systems. The developed approach contributes to reducing diagnostic errors and accelerating decision-making in intensive care settings.