Dynamic Patient Triage Optimization in Healthcare Settings Using RNNs for Decision Support

Abstract

The article presents a sophisticated healthcare system that uses recurrent neural networks (RNNs) to optimize real-time patient triage. The developed model integrates patient data from the Internet of Things (IoT), and it performs dynamic assessments of vital indicators like heart rate, blood pressure, and temperature to prioritize pre-operation care based on the urgency and severity of diseases. The RNN architecture considers the temporal connections included in the data, which enables a more sophisticated comprehension of the changing patient states. The training and evaluation of the model make use of a large dataset. In comparison to more conventional triage methods, the model displays considerable gains in both accuracy and efficiency. The system not only reduces the amount of time needed to respond and allocate resources, but it also improves the flexibility to react to shifting patient conditions. This research represents a significant step toward the development of intelligent decision support systems in the healthcare industry. It demonstrates the potential of more sophisticated machine learning approaches to transform patient care processes in environments with high stakes and a dynamic nature.