Artificial Intelligence aware Knowledge Graphs and Deep Learning-Based Diagnostic Prediction Model in Healthcare

Diagnostic prediction experts use a patient’s medical history to anticipate their future diagnosis. Attention mechanisms and recurrent neural networks are typically utilized for this purpose; however, data shortages often cause complications. Additionally, healthcare information can help diagnostic prediction algorithms. This study links healthcare ideas using AI-aware, intelligent healthcare ontologies and patient visit records as an intelligent graph. Next, utilize a graph convolution module to collect geographical attributes that show patient visit healthcare code linkages. Using a multihead attention technique, visit information and healthcare knowledge are compared to predict the patient’s future health. On two open-source healthcare datasets, the suggested solution outperforms state-of-the-art diagnostic prediction algorithms and better uses IMG data.