Enhancing ICU Monitoring with Predictive Analytics Using Random Forests and Long Short-Term Memory Networks

Abstract

This research investigates the integration of predictive analytics into Intensive Care Unit (ICU) monitoring systems, utilizing Random Forests and Long Short-Term Memory (LSTM) networks to enhance patient outcome predictions. The ICU environment is characterized by high data complexity and critical care requirements, necessitating advanced analytical models to improve decision-making processes. In this study, we leverage electronic health records and real-time physiological data to develop a hybrid model combining the strengths of Random Forests for feature selection and interpretability with LSTMs' ability to capture temporal dependencies. The model aims to predict critical events, such as sepsis onset and patient deterioration, to enable timely interventions. We conducted extensive experiments on a large, anonymized dataset from multiple ICUs, assessing the model's accuracy, sensitivity, and specificity in comparison to existing methods. Our hybrid approach demonstrated improved predictive performance, achieving an AUROC of 0.92, indicating a significant enhancement over baseline models. Furthermore, the use of Random Forests enabled effective dimensionality reduction and feature importance ranking, aiding clinicians in understanding key contributing factors. The LSTM component facilitated robust temporal pattern recognition, accommodating the dynamic nature of patient data. This study underscores the potential of combining machine learning techniques to augment ICU monitoring capabilities, ultimately aiming to decrease morbidity and mortality rates through proactive care strategies. Future work will focus on real-world implementation challenges and user interface development to ensure seamless integration into clinical workflows.