Sepsis Prediction Using Graph Convolutional Networks over Patient-Feature-Value Triplets

• URL: http://arxiv.org/abs/2512.05416v1
• Date: Fri, 05 Dec 2025 04:30:48 GMT
• Title: Sepsis Prediction Using Graph Convolutional Networks over Patient-Feature-Value Triplets
• Authors: Bozhi Dan, Di Wu, Ji Xu, Xiang Liu, Yiziting Zhu, Xin Shu, Yujie Li, Bin Yi,
• Abstract summary: We propose Triplet-GCN, a single-branch graph convolutional model that represents each encounter as patient--feature--value triplets.<n>We learn patient embeddings via a Graph Convolutional Network (GCN) followed by a lightweight multilayer perceptron (MLP)
• Score: 10.151360630975482
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the intensive care setting, sepsis continues to be a major contributor to patient illness and death; however, its timely detection is hindered by the complex, sparse, and heterogeneous nature of electronic health record (EHR) data. We propose Triplet-GCN, a single-branch graph convolutional model that represents each encounter as patient--feature--value triplets, constructs a bipartite EHR graph, and learns patient embeddings via a Graph Convolutional Network (GCN) followed by a lightweight multilayer perceptron (MLP). The pipeline applies type-specific preprocessing -- median imputation and standardization for numeric variables, effect coding for binary features, and mode imputation with low-dimensional embeddings for rare categorical attributes -- and initializes patient nodes with summary statistics, while retaining measurement values on edges to preserve "who measured what and by how much". In a retrospective, multi-center Chinese cohort (N = 648; 70/30 train--test split) drawn from three tertiary hospitals, Triplet-GCN consistently outperforms strong tabular baselines (KNN, SVM, XGBoost, Random Forest) across discrimination and balanced error metrics, yielding a more favorable sensitivity--specificity trade-off and improved overall utility for early warning. These findings indicate that encoding EHR as triplets and propagating information over a patient--feature graph produce more informative patient representations than feature-independent models, offering a simple, end-to-end blueprint for deployable sepsis risk stratification.

Related papers

• Structured Spectral Graph Representation Learning for Multi-label Abnormality Analysis from 3D CT Scans [0.0]
  Multi-label classification of 3D Chest CT scans remains a critical yet challenging problem.<n>Existing methods based on 3D convolutional neural networks struggle to capture long-range dependencies.<n>We propose a new graph-based framework that represents 3D CT volumes as structured graphs.
  arXiv  Detail & Related papers  (2025-10-12T19:49:51Z)
• TRELLIS-Enhanced Surface Features for Comprehensive Intracranial Aneurysm Analysis [2.624902795082451]
  Intracranial aneurysms pose a significant clinical risk yet are difficult to detect, delineate and model due to limited annotated 3D data.<n>We propose a cross-domain feature-transfer approach that leverages the latent geometric embeddings learned by TRELLIS, a generative model trained on large-scale non-medical 3D datasets.
  arXiv  Detail & Related papers  (2025-09-03T07:51:17Z)
• Bridging Graph and State-Space Modeling for Intensive Care Unit Length of Stay Prediction [14.897714298219569]
  We propose S$2$G-Net, a novel neural architecture that unifies state-space sequence modeling with multi-view Graph Neural Networks (GNNs) for ICU LOS prediction.<n> Experiments on the large-scale MIMIC-IV cohort dataset show that S$2$G-Net consistently outperforms sequence models.
  arXiv  Detail & Related papers  (2025-08-24T23:32:25Z)
• A Generative Framework for Predictive Modeling of Multiple Chronic Conditions Using Graph Variational Autoencoder and Bandit-Optimized Graph Neural Network [0.0]
  Predicting the emergence of multiple chronic conditions (MCC) is crucial for early intervention and personalized healthcare.<n>Graph neural networks (GNNs) are effective methods for modeling complex graph data, such as those found in MCC.<n>We propose a novel generative framework for GNNs that constructs a representative underlying graph structure by utilizing the distribution of the data.
  arXiv  Detail & Related papers  (2024-09-20T17:26:38Z)
• TokenUnify: Scaling Up Autoregressive Pretraining for Neuron Segmentation [65.65530016765615]
  We propose a hierarchical predictive coding framework that captures multi-scale dependencies through three complementary learning objectives.<n> TokenUnify integrates random token prediction, next-token prediction, and next-all token prediction to create a comprehensive representational space.<n>We also introduce a large-scale EM dataset with 1.2 billion annotated voxels, offering ideal long-sequence visual data with spatial continuity.
  arXiv  Detail & Related papers  (2024-05-27T05:45:51Z)
• Predicting Infant Brain Connectivity with Federated Multi-Trajectory GNNs using Scarce Data [54.55126643084341]
  Existing deep learning solutions suffer from three major limitations. We introduce FedGmTE-Net++, a federated graph-based multi-trajectory evolution network. Using the power of federation, we aggregate local learnings among diverse hospitals with limited datasets.
  arXiv  Detail & Related papers  (2024-01-01T10:20:01Z)
• AdaMedGraph: Adaboosting Graph Neural Networks for Personalized Medicine [31.424781716926848]
  We propose a novel algorithm named ours, which can automatically select important features to construct multiple patient similarity graphs. ours is evaluated on two real-world medical scenarios and shows superiors performance.
  arXiv  Detail & Related papers  (2023-11-24T06:27:25Z)
• Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation [133.02190910009384]
  We propose a two-stream graph convolutional network (i.e., TSGCN) to handle inter-view confusion between different raw attributes. Our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation.
  arXiv  Detail & Related papers  (2022-04-19T10:41:09Z)
• Generalizing electrocardiogram delineation: training convolutional neural networks with synthetic data augmentation [63.51064808536065]
  Existing databases for ECG delineation are small, being insufficient in size and in the array of pathological conditions they represent. This article delves has two main contributions. First, a pseudo-synthetic data generation algorithm was developed, based in probabilistically composing ECG traces given "pools" of fundamental segments, as cropped from the original databases, and a set of rules for their arrangement into coherent synthetic traces. Second, two novel segmentation-based loss functions have been developed, which attempt at enforcing the prediction of an exact number of independent structures and at producing closer segmentation boundaries by focusing on a reduced number of samples.
  arXiv  Detail & Related papers  (2021-11-25T10:11:41Z)
• Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data [62.29031007761901]
  This paper proposes a novel contrastive regularized clinical classification model. We introduce two unique positive sampling strategies specifically tailored for EHR data. Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
  arXiv  Detail & Related papers  (2021-04-07T06:02:04Z)
• BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes [53.163089893876645]
  We propose a novel self-attention mechanism that captures the contextual dependency and temporal relationships within a patient's healthcare journey. An end-to-end bidirectional temporal encoder network (BiteNet) then learns representations of the patient's journeys. We have evaluated the effectiveness of our methods on two supervised prediction and two unsupervised clustering tasks with a real-world EHR dataset.
  arXiv  Detail & Related papers  (2020-09-24T00:42:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.