The ALICIA project aimed to integrate Artificial Intelligence (AI) into intensive care unit (ICU) management, improving patient monitoring, diagnosis, and treatment through big data analysis. Machine learning algorithms were developed to analyze large-scale clinical data, enhancing patient outcome predictions and supporting timely clinical decisions.
A key innovation was the use of data visualization techniques, such as heat maps, to detect anomalies in vital signs. During the COVID-19 pandemic, these tools revealed increased episodes of respiratory distress, desaturation, and fever, enabling early detection of clinical deterioration and faster interventions.
The project also developed predictive models to estimate key clinical outcomes, such as length of stay, mortality, and complications, optimizing resource allocation and ICU efficiency. Machine learning algorithms outperformed traditional assessments, offering valuable decision support.
Despite its potential, AI implementation in intensive care faces challenges, including the need for high-quality data, seamless integration with clinical systems, and effective training for healthcare professionals. Addressing these barriers is crucial for AI-driven innovations to enhance critical care.