Knowledge Graph-Driven Clinical Decision Intelligence System for Personalized Treatment Optimization

Abstract

The increased complexity and heterogeneity of healthcare data demand smart systems that have the ability to model relationships amongst clinical entities to facilitate accurate and personalized decisions. The conventional machine learning and deep learning methods do not adequately address the complex relationship among patients, diseases, symptoms, and treatment and their application in clinical settings is limited. The research will introduce a clinical decision intelligence system that optimizes personalized treatment using a knowledge graph to combine the structured graph representation with graph neural network (GNN)-based learning. The longitudinal electronic health record (EHR) data is used to build a comprehensive knowledge graph, with nodes representing clinical entities and the relationships defined by the edges representing semantic and correlation-based relationships. The suggested framework uses representation learning through graphs to understand contextual interactions, which allows better predictions of the most effective treatment strategies. The model is tested on the MIMIC-III dataset and compared to the baseline models, such as logistic regression, random forest, LSTM, and attention-based models. The experiments have shown that the new method has better performance (in accuracy, F1-score, and AUC) than other methods. Also, the graph-based model can be utilized to make interpretable decisions based on the relational reasoning scheme, which facilitates explainable clinical decisions. The intended system provides a scalable and efficient solution towards advanced clinical decision support in precision medicine and intelligent healthcare analytics.