OPTIMIZING IOMT BASED SMART HEALTHCARE WITH DEEP EHR-TANFLOW: A HYBRID ATTENTION DRIVEN MODEL

This project presents a structured implementation framework for an Internet of Medical Things (IoMT) based smart healthcare monitoring system utilizing the Deep EHR-TANFlow architecture. Designed to address challenges in processing structured Electronic Health Records (EHR), the system integrates a comprehensive data preprocessing pipeline and a hybrid deep learning model. The preprocessing stage employs K-Nearest Neighbors (KNN) imputation for handling missing clinical values, Min-Max normalization, and a sliding-window segmentation technique to capture temporal dynamics. The core predictive engine, designated as the CNN-BiGRU Temporal Attention Network (CG-TAN), combines Convolutional Neural Networks (CNN) for localized spatial feature extraction with Bidirectional Gated Recurrent Units (Bi-GRU) to model bidirectional temporal dependencies. Furthermore, a Self-Attention (SA)

mechanism is integrated to prioritize critical clinical events and enhance global contextual representation. This implementation guidance supports undergraduate engineering students in developing robust, non-invasive predictive models for healthcare monitoring, offering a practical approach to handling multi-variate clinical time-series data. The framework provides a systematic methodology for evaluating model performance against standard clinical metrics, facilitating research direction and component guidance in smart healthcare applications.

Message: Hi FE, I need help with “OPTIMIZING IOMT BASED SMART HEALTHCARE WITH DEEP EHR-TANFLOW: A HYBRID ATTENTION DRIVEN MODEL” in “Biotechnology & Biomedical Engineering”