Semi-Supervised Graph Representation Learning with Human-centric Explanation for Predicting Fatty Liver Disease

• URL: http://arxiv.org/abs/2403.02786v1
• Date: Tue, 5 Mar 2024 08:59:45 GMT
• Title: Semi-Supervised Graph Representation Learning with Human-centric Explanation for Predicting Fatty Liver Disease
• Authors: So Yeon Kim, Sehee Wang, Eun Kyung Choe
• Abstract summary: This study explores the potential of graph representation learning within a semi-supervised learning framework. Our approach constructs a subject similarity graph to identify risk patterns from health checkup data.
• Score: 2.992602379681373
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Addressing the challenge of limited labeled data in clinical settings, particularly in the prediction of fatty liver disease, this study explores the potential of graph representation learning within a semi-supervised learning framework. Leveraging graph neural networks (GNNs), our approach constructs a subject similarity graph to identify risk patterns from health checkup data. The effectiveness of various GNN approaches in this context is demonstrated, even with minimal labeled samples. Central to our methodology is the inclusion of human-centric explanations through explainable GNNs, providing personalized feature importance scores for enhanced interpretability and clinical relevance, thereby underscoring the potential of our approach in advancing healthcare practices with a keen focus on graph representation learning and human-centric explanation.

Related papers

• Evaluating the Predictive Features of Person-Centric Knowledge Graph Embeddings: Unfolding Ablation Studies [0.757843972001219]
  We propose a systematic approach to examine the results of GNN models trained with structured and unstructured information from the MIMIC-III dataset. We show the robustness of this approach in identifying predictive features in PKGs for the task of readmission prediction.
  arXiv  Detail & Related papers  (2024-08-27T09:48:25Z)
• Parkinson's Disease Detection from Resting State EEG using Multi-Head Graph Structure Learning with Gradient Weighted Graph Attention Explanations [9.544065991313062]
  We propose a novel graph neural network (GNN) technique for explainable Parkinson's disease (PD) detection using resting state EEG. We employ structured global convolutions with contrastive learning to better model complex features with limited data. We developed and evaluated our method using the UC San Diego Parkinson's disease EEG dataset, and achieved 69.40% detection accuracy in subject-wise leave-one-out cross-validation.
  arXiv  Detail & Related papers  (2024-08-01T20:54:33Z)
• MLIP: Enhancing Medical Visual Representation with Divergence Encoder and Knowledge-guided Contrastive Learning [48.97640824497327]
  We propose a novel framework leveraging domain-specific medical knowledge as guiding signals to integrate language information into the visual domain through image-text contrastive learning. Our model includes global contrastive learning with our designed divergence encoder, local token-knowledge-patch alignment contrastive learning, and knowledge-guided category-level contrastive learning with expert knowledge. Notably, MLIP surpasses state-of-the-art methods even with limited annotated data, highlighting the potential of multimodal pre-training in advancing medical representation learning.
  arXiv  Detail & Related papers  (2024-02-03T05:48:50Z)
• Classification of developmental and brain disorders via graph convolutional aggregation [6.6356049194991815]
  We introduce an aggregator normalization graph convolutional network by leveraging aggregation in graph sampling. The proposed model learns discriminative graph node representations by incorporating both imaging and non-imaging features into the graph nodes and edges. We benchmark our model against several recent baseline methods on two large datasets, Autism Brain Imaging Data Exchange (ABIDE) and Alzheimer's Disease Neuroimaging Initiative (ADNI)
  arXiv  Detail & Related papers  (2023-11-13T14:36:29Z)
• Graph AI in Medicine [9.733108180046555]
  Graph neural networks (GNNs) process data holistically by viewing modalities as nodes interconnected by their relationships. GNNs capture information through localized neural transformations defined on graph relationships. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge.
  arXiv  Detail & Related papers  (2023-10-20T19:01:01Z)
• DEGREE: Decomposition Based Explanation For Graph Neural Networks [55.38873296761104]
  We propose DEGREE to provide a faithful explanation for GNN predictions. By decomposing the information generation and aggregation mechanism of GNNs, DEGREE allows tracking the contributions of specific components of the input graph to the final prediction. We also design a subgraph level interpretation algorithm to reveal complex interactions between graph nodes that are overlooked by previous methods.
  arXiv  Detail & Related papers  (2023-05-22T10:29:52Z)
• Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report Generation [92.73584302508907]
  We propose a knowledge graph with Dynamic structure and nodes to facilitate medical report generation with Contrastive Learning. In detail, the fundamental structure of our graph is pre-constructed from general knowledge. Each image feature is integrated with its very own updated graph before being fed into the decoder module for report generation.
  arXiv  Detail & Related papers  (2023-03-18T03:53:43Z)
• IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction [47.999621481852266]
  We propose an interpretable graph learning-based model which interprets the clinical relevance of the input features towards the task. In a clinical scenario, such a model can assist the clinical experts in better decision-making for diagnosis and treatment planning. Our proposed model shows superior performance with respect to compared methods with an increase in an average accuracy of 3.2% for Tadpole, 1.6% for UKBB Gender, and 2% for the UKBB Age prediction task.
  arXiv  Detail & Related papers  (2021-03-29T13:04:02Z)
• Finding Patient Zero: Learning Contagion Source with Graph Neural Networks [67.3415507211942]
  Locating the source of an epidemic can provide critical insights into the infection's transmission course. Existing methods use graph-theoretic measures and expensive message-passing algorithms. We revisit this problem using graph neural networks (GNNs) to learn P0.
  arXiv  Detail & Related papers  (2020-06-21T21:12:44Z)
• Graph Representation Learning via Graphical Mutual Information Maximization [86.32278001019854]
  We propose a novel concept, Graphical Mutual Information (GMI), to measure the correlation between input graphs and high-level hidden representations. We develop an unsupervised learning model trained by maximizing GMI between the input and output of a graph neural encoder.
  arXiv  Detail & Related papers  (2020-02-04T08:33:49Z)

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.