Evaluating the Predictive Features of Person-Centric Knowledge Graph Embeddings: Unfolding Ablation Studies

• URL: http://arxiv.org/abs/2408.15294v2
• Date: Thu, 29 Aug 2024 09:43:04 GMT
• Title: Evaluating the Predictive Features of Person-Centric Knowledge Graph Embeddings: Unfolding Ablation Studies
• Authors: Christos Theodoropoulos, Natasha Mulligan, Joao Bettencourt-Silva,
• Abstract summary: 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.
• Score: 0.757843972001219
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Developing novel predictive models with complex biomedical information is challenging due to various idiosyncrasies related to heterogeneity, standardization or sparseness of the data. We previously introduced a person-centric ontology to organize information about individual patients, and a representation learning framework to extract person-centric knowledge graphs (PKGs) and to train Graph Neural Networks (GNNs). In this paper, we propose a systematic approach to examine the results of GNN models trained with both structured and unstructured information from the MIMIC-III dataset. Through ablation studies on different clinical, demographic, and social data, we show the robustness of this approach in identifying predictive features in PKGs for the task of readmission prediction.

Related papers

• TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits for Disease Subtyping based on EHR Data [42.96821770394798]
  TACCO is a novel framework that jointly discovers clusters of clinical concepts and patient visits based on a hypergraph modeling of EHR data. We conduct experiments on the public MIMIC-III dataset and Emory internal CRADLE dataset over the downstream clinical tasks of phenotype classification and cardiovascular risk prediction. In-depth model analysis, clustering results analysis, and clinical case studies further validate the improved utilities and insightful interpretations delivered by TACCO.
  arXiv  Detail & Related papers  (2024-06-14T14:18:38Z)
• Investigation of Customized Medical Decision Algorithms Utilizing Graph Neural Networks [15.04251924479172]
  This paper introduces a personalized medical decision algorithm utilizing graph neural network (GNN) The proposed personalized medical decision algorithm showed significantly superior performance in terms of disease prediction accuracy, treatment effect evaluation and patient risk stratification.
  arXiv  Detail & Related papers  (2024-05-23T04:30:41Z)
• Semi-Supervised Graph Representation Learning with Human-centric Explanation for Predicting Fatty Liver Disease [2.992602379681373]
  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.
  arXiv  Detail & Related papers  (2024-03-05T08:59:45Z)
• Recent Advances in Predictive Modeling with Electronic Health Records [71.19967863320647]
  utilizing EHR data for predictive modeling presents several challenges due to its unique characteristics. Deep learning has demonstrated its superiority in various applications, including healthcare.
  arXiv  Detail & Related papers  (2024-02-02T00:31:01Z)
• Knowledge Graph Representations to enhance Intensive Care Time-Series Predictions [4.660203987415476]
  Our proposed methodology integrates medical knowledge with ICU data, improving clinical decision modeling. It combines graph representations with vital signs and clinical reports, enhancing performance. Our model includes an interpretability component to understand how knowledge graph nodes affect predictions.
  arXiv  Detail & Related papers  (2023-11-13T09:11:55Z)
• 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)
• Deep Co-Attention Network for Multi-View Subspace Learning [73.3450258002607]
  We propose a deep co-attention network for multi-view subspace learning. It aims to extract both the common information and the complementary information in an adversarial setting. In particular, it uses a novel cross reconstruction loss and leverages the label information to guide the construction of the latent representation.
  arXiv  Detail & Related papers  (2021-02-15T18:46:44Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)
• Trajectories, bifurcations and pseudotime in large clinical datasets: applications to myocardial infarction and diabetes data [94.37521840642141]
  We suggest a semi-supervised methodology for the analysis of large clinical datasets, characterized by mixed data types and missing values. The methodology is based on application of elastic principal graphs which can address simultaneously the tasks of dimensionality reduction, data visualization, clustering, feature selection and quantifying the geodesic distances (pseudotime) in partially ordered sequences of observations.
  arXiv  Detail & Related papers  (2020-07-07T21:04:55Z)
• Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes [102.02672176520382]
  Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals. In electronic health records we can observe the different diseases a patient has, but can only infer the temporal relationship between each co-morbid condition. We develop deep diffusion processes to model "dynamic comorbidity networks"
  arXiv  Detail & Related papers  (2020-01-08T15:47:08Z)

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.