MedGCN: An IoT-edge thrombus graph convolutional network for accurate prediction and prescription diagnosis of vascular occlusive diseases from unstructured clinical reports

Abstract

The timely and accurate prediction and diagnosis of vascular occlusive diseases are pivotal in enhancing patient outcomes. This research addresses this critical healthcare challenge by introducing MedGCN, a novel thrombus graph convolutional network model. MedGCN is specifically designed for the precise prediction of prescriptions and diagnoses based on unstructured clinical diagnostic reports. The model synergistically blends OpenAI’s GPT4 and a uniquely designed Cross Fusion Graph Convolutional Network (CF-GCN) to ensure the meticulous fusion of knowledge. We delve into nine distinct learning tasks, encompassing both prescription and diagnosis and employ a multi-label classification GCN pretraining technique to assess them. Our evaluation underscores MedGCN’s robust predictive prowess across various tasks. By amalgamating cutting-edge AI paradigms with IoT edge computing, this research not only bolsters the efficacy of medical decision-making but also champions patient privacy. The methodologies and findings presented herein hold immense potential for deployment in IoT frameworks, thus proffering swift and precise assistance in medical decision-making and addressing a paramount need in the contemporary healthcare landscape.