International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 04 | Apr 2025
p-ISSN: 2395-0072
www.irjet.net
Adaptive AI Systems for Real-time Medical Decision Support in Critical Care Shubham Gupta Noblesoft Solutions Inc, San Antonio, Texas, USA -----------------------------------------------------------------------***-----------------------------------------------------------------------Abstract Critical care environments present unique challenges for medical decision-making due to the high-stakes nature of decisions, unpredictable patient conditions, and time-sensitive circumstances. This paper introduces the Adaptive Critical Care AI Support (ACAIS) system, a novel framework for real-time medical decision support that continuously adapts to changing patient conditions and clinical environments. ACAIS integrates multimodal physiological data streams with a hybrid AI architecture combining deep learning, reinforcement learning, and explainable AI techniques. Our approach demonstrates significant improvements in decision quality metrics across various critical care scenarios, with performance gains of 23% in prediction accuracy and 31% reduction in time-to-decision compared to traditional rule-based systems. The system's adaptive capabilities enable personalized treatment recommendations while maintaining interpretability for clinicians. We demonstrate ACAIS's efficacy through evaluation on retrospective critical care data and a preliminary implementation in a simulated ICU environment.
Keywords: Medical Decision Support, Adaptive AI, Critical Care, Real-time Systems, Deep Learning, Clinical Decision Support I. Introduction Critical care medicine demands rapid, accurate decision-making in high-stress environments where patient conditions can deteriorate rapidly and unpredictably. Traditional clinical decision support systems (CDSS) struggle with the dynamic nature of these environments, often utilizing static rule-based models that fail to account for individual patient variability and evolving clinical conditions [1]. While recent advances in artificial intelligence (AI) have shown promise for enhancing medical decision-making, most current implementations lack the adaptability required for critical care settings [2]. The complexity of critical care environments poses several challenges for AI-based systems: 1. 2. 3. 4. 5.
Time-sensitive decisions with incomplete information Highly variable patient responses to interventions Complex interdependencies between physiological systems Need for continuous adaptation to changing conditions Requirement for interpretable recommendations to maintain clinician trust
This paper presents the Adaptive Critical Care AI Support (ACAIS) system, designed to address these challenges through a novel approach to real-time medical decision support. ACAIS integrates multimodal physiological data with adaptive learning algorithms that continuously refine their models based on patient responses and clinical outcomes. The system maintains explainability of its recommendations to ensure clinician trust and facilitate regulatory compliance. The primary contributions of this work include: 1. 2. 3. 4. 5.
A comprehensive architecture for adaptive AI-based decision support in critical care Novel methods for real-time integration and analysis of multimodal clinical data A hybrid learning approach combining deep learning with reinforcement learning for adaptive decision support Techniques for maintaining explainability while allowing for model adaptation Evaluation of system performance on retrospective clinical data and in simulated environments
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