IoT-Connected Telehealth Environments with Long Short-Term Memory Networks for Precise Time-Series Patient Behavior Analysis

Abstract

Advanced analytics are becoming more important in telehealth environments as the number of connected devices continues to rise, necessitating the collection of massive amounts of time-series patient data. This system uses Long Short-Term Memory (LSTM) networks in telemedicine that are linked to the Internet of Things (IoT), with an emphasis on accurate longitudinal monitoring of patient behavior. An extensive dataset gathered from IoT devices that track several health metrics in real-time is used in the research. LSTM networks are used to detect patterns and relationships in time-series data using a deep learning technique. In turn, this allows the system to analyze patient behavior with more precision and speed. IoT provides seamless connections for real-time data transfer it offers continuous monitoring within the telehealth framework. The LSTM-based model improves our comprehension of patient behavior, which in turn enables us to spot outliers, forecast health trends, and provide individualized healthcare recommendations. Through its demonstration of LSTM networks' usefulness in the context of IoT-connected telehealth settings, the study makes a valuable contribution to the rapidly growing area of digital health. Accurate time-series analysis has the ability to improve patient monitoring, early intervention, and healthcare outcomes, as shown by the results.