Deep Learning-Based Real-Time Interfaces for Monitoring and Predictive Control of Intensive Care Unit Patient Parameters
Abstract
The end goal of this work is to develop a complete system that uses deep learning to keep an eye on and predict data in real time for patients in intensive care units. With the assistance provided by this project, monitoring patients on time can become more accurate and consistent. After giving the networks their anticipated parameters and risk ratings, their performance is enhanced by using a number of methods, such as residual weighting, temporal smoothing, and multi-objective optimization, to name a few. This happens right after the networks finish the prediction operation. Medical personnel should be able to make better judgments by using predictive control methods such as proportional-derivative adjustments, trend analysis, and anomaly detection. If we use these methods, we can fix the problems and turn on the alerts. After all the processing and modeling were done, it looks like the prediction's dependability, the reduction's accuracy, the warning's timeliness, the signal quality, and the calculation's efficiency have all gone up a lot. The recommended method could provide better care for patients in the critical care unit. It ensures that trajectories align with the body's functions, alarms trigger quickly, and real-time data analysis occurs swiftly. These results suggest that the paradigm might make critical care organizations perform better, help uncover problems sooner, and make patients safer. We may then utilize this paradigm to develop sophisticated, data-driven systems for monitoring patients in intensive care units.