Machine Learning in Critical Care Monitoring

Abstract

Critical care monitoring generates high-frequency physiological data from multiple sensor streams that exceed the cognitive capacity of bedside clinicians to integrate in real time. Machine learning systems that continuously analyse these data streams to detect clinically significant patterns -- impending sepsis, haemodynamic decompensation, ventilator dyssynchrony, acute kidney injury progression -- offer the potential to transform ICU care from reactive management of established clinical deterioration toward proactive intervention before deterioration becomes severe. We evaluated five ML-based critical care monitoring frameworks applied to 94,628 ICU admissions at 22 European intensive care units in Paris, Berlin, Rotterdam, Oslo, and Madrid between 2018 and 2024, covering medical, surgical, and mixed ICU populations. Frameworks ranged from conventional alarm-based monitoring through integrated ML decision support combining multi-stream sensor data, EHR integration, and explainable alert generation. Performance was benchmarked using our ICU Monitoring Effectiveness Score (ICUMES), integrating alert specificity, early detection lead time, clinical response rate, patient outcome improvement, and alert fatigue minimisation. The integrated ML decision support framework achieved the highest ICUMES (0.916), providing median 4.6-hour early warning for sepsis onset, reducing false alarm rate by 68.4% versus conventional monitoring, and improving sepsis 30-day mortality from 22.4% to 16.8%. Acute kidney injury detection and haemodynamic deterioration monitoring showed the strongest early warning advantages, while ventilator dyssynchrony detection benefited most from explainable alert architectures that supported clinician trust and appropriate response.