AI-Driven Patient Monitoring and Real-Time Clinical Research: Leveraging FHIR-Based Data Lake Architectures and IoT-Enabled Wearable Devices for Precision Medicine

Abstract

In order to provide predictive healthcare, this research introduces an AI-driven Remote Patient Monitoring (RPM) framework that combines wearable technology provided by the Internet of Things with sophisticated machine learning frameworks. The suggested method uses temporal convolutional networks (TCNs), transformer-based time-series encoders, and survival analysis models to allow proactive, real-time patient risk assessment, in contrast to conventional monitoring systems that depend on threshold-based alarms. The method was tested on a dataset of 5,000 patients who were tracked using pulse oximeters, glucose monitors, ECG patches, and smartwatches. According to experimental outcomes, our structure outperformed current techniques like Cox proportional hazards (0.72) and LSTM baselines (82% accuracy) with a prediction accuracy of 91% for short-horizon risk forecasting and a C- index of 0.80 in survival evaluation. With a silhouette score of 0.64, unsupervised clustering of patient states enhanced subgroup identification and allowed for more targeted therapies. Additionally, our dual deployment approach showed high-throughput server scalability (600 requests/s) and low- latency edge performance (120 ms). Clinical reliability was greatly enhanced by model calibration, which decreased the Expected Calibration Error to 0.04 while preserving good recall (0.90) and reducing false alarms (7%). All things considered, the suggested AI-RPM system surpasses conventional methods by improving prediction accuracy, cutting latency, and providing scalable, customised, and reliable remote healthcare solutions.

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