Development and software implementation of an intelligent decision support system for choosing mechanical ventilation modes

The relevance of this study is due to the high cognitive load on the intensivist when choosing a mechanical ventilation mode because of dozens of modes and more than 50 adjustable parameters. The lack of formalized criteria and transparent decision support systems leads to variability in therapy and an increased risk of ventilator-associated complications. The aim of this work is to develop a methodology, algorithm and software prototype of an intelligent clinical decision support system for choosing mechanical ventilation modes, providing explainability of recommendations. The methods used include formalization of clinical parameters, construction of a state classifier, creation of production rules, object-oriented programming in Python, and testing on literature-based clinical scenarios. Four groups of parameters (respiratory mechanics, gas exchange, patient activity, hemodynamics) are formalized. A state classifier (apnea, respiratory muscle weakness, obstructive and restrictive disorders) is built. A knowledge base of 30 "IF–THEN" production rules grouped into six clinical situations with priority selection is created. An algorithm with an explanation module that generates a textual justification for each recommendation is developed. The software prototype is implemented in Python (tkinter, experta). Testing on 20 literature‑based clinical scenarios showed complete agreement of recommendations with reference protocols in 85 % of cases, partial agreement in 10 %, and disagreement (a scenario requiring extracorporeal CO₂ removal) in 5 %. The average recommendation generation time was 0.42 seconds. The obtained results confirm the feasibility of the proposed methodology and allow the system to be used as a standalone physician’s workstation or as an embeddable module in ventilators.

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