Chronic postsurgical pain (CPSP) affects 10–50% of surgical patients and is a major contributor to long-term opioid use and reduced quality of life. Current predictive models treat patients independently and fail to capture how risk evolves over time or how postoperative opioid trajectories influence divergence in outcomes. We propose a dynamic graph neural network (GNN) framework in which patients are modeled as nodes and similarity-based edges evolve over time based on opioid prescription patterns, pain scores, and preoperative psychological factors. The model includes (1) a patient graph with static preoperative features, (2) a temporal edge update mechanism, (3) a GNN message-passing layer that aggregates information from dynamically connected patients, and (4) a prediction head estimating CPSP risk at 3, 6, and 12 months. By modeling changing patient relationships after surgery, the framework captures how similar patients may diverge or converge depending on postoperative management, enabling more accurate and personalized CPSP risk prediction using longitudinal electronic health record data.
Rare pediatric cancers are difficult to treat due to their very low incidence, which limits drug development and makes experimental screening of therapies slow, costly, and dependent on scarce tumor samples. Traditional supervised machine learning approaches are also constrained by the lack of labeled drug–response data, while rich but unlabeled protein–protein interaction networks remain underutilized. We propose a self-supervised graph representation learning framework that integrates protein interaction networks with patient gene expression data to support drug repurposing. The model builds a heterogeneous graph of drugs, genes, diseases, and proteins, and uses a graph neural network trained with self-supervised objectives such as contrastive learning and masked prediction to learn molecular representations without labeled data. It is then fine-tuned on small pediatric cancer datasets. The framework enables prediction of candidate drug therapies by combining learned biological network representations with disease-specific expression profiles. This approach reduces reliance on large labeled datasets and allows adaptation to rare cancer contexts, offering a scalable strategy for computational drug repurposing in pediatric oncology.
Clinical Intelligence Research Press