Hypergraph Convolutional Networks for Fine-grained ICU Patient Similarity Analysis and Risk Prediction

• URL: http://arxiv.org/abs/2308.12575v2
• Date: Sat, 21 Oct 2023 04:54:00 GMT
• Title: Hypergraph Convolutional Networks for Fine-grained ICU Patient Similarity Analysis and Risk Prediction
• Authors: Yuxi Liu, Zhenhao Zhang, Shaowen Qin, Flora D. Salim, Antonio Jimeno Yepes, Jun Shen, Jiang Bian
• Abstract summary: The Intensive Care Unit (ICU) is one of the most important parts of a hospital, which admits critically ill patients and provides continuous monitoring and treatment. Various patient outcome prediction methods have been attempted to assist healthcare professionals in clinical decision-making.
• Score: 15.06049250330114
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Intensive Care Unit (ICU) is one of the most important parts of a hospital, which admits critically ill patients and provides continuous monitoring and treatment. Various patient outcome prediction methods have been attempted to assist healthcare professionals in clinical decision-making. Existing methods focus on measuring the similarity between patients using deep neural networks to capture the hidden feature structures. However, the higher-order relationships are ignored, such as patient characteristics (e.g., diagnosis codes) and their causal effects on downstream clinical predictions. In this paper, we propose a novel Hypergraph Convolutional Network that allows the representation of non-pairwise relationships among diagnosis codes in a hypergraph to capture the hidden feature structures so that fine-grained patient similarity can be calculated for personalized mortality risk prediction. Evaluation using a publicly available eICU Collaborative Research Database indicates that our method achieves superior performance over the state-of-the-art models on mortality risk prediction. Moreover, the results of several case studies demonstrated the effectiveness and robustness of the model decisions.

Related papers

• Deep State-Space Generative Model For Correlated Time-to-Event Predictions [54.3637600983898]
  We propose a deep latent state-space generative model to capture the interactions among different types of correlated clinical events. Our method also uncovers meaningful insights about the latent correlations among mortality and different types of organ failures.
  arXiv  Detail & Related papers  (2024-07-28T02:42:36Z)
• SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing [67.8991481023825]
  Sepsis is the leading cause of in-hospital mortality in the USA. Existing predictive models are usually trained on high-quality data with few missing information. For the potential high-risk patients with low confidence due to limited observations, we propose a robust active sensing algorithm.
  arXiv  Detail & Related papers  (2024-07-24T04:47:36Z)
• A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis [3.5280004326441365]
  PatWay-Net is an ML framework designed for interpretable predictions of admission to the intensive care unit for patients with sepsis. We propose a novel type of recurrent neural network and combine it with multi-layer perceptrons to process the patient pathways. We demonstrate its utility through a comprehensive dashboard that visualizes patient health trajectories, predictive outcomes, and associated risks.
  arXiv  Detail & Related papers  (2024-05-21T20:31:42Z)
• A Knowledge Distillation Approach for Sepsis Outcome Prediction from Multivariate Clinical Time Series [2.621671379723151]
  We use knowledge distillation via constrained variational inference to distill the knowledge of a powerful "teacher" neural network model. We train a "student" latent variable model to learn interpretable hidden state representations to achieve high predictive performance for sepsis outcome prediction.
  arXiv  Detail & Related papers  (2023-11-16T05:06:51Z)
• MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation [58.93221876843639]
  This paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion. It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space. It discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data.
  arXiv  Detail & Related papers  (2023-10-04T01:36:30Z)
• Automatic diagnosis of knee osteoarthritis severity using Swin transformer [55.01037422579516]
  Knee osteoarthritis (KOA) is a widespread condition that can cause chronic pain and stiffness in the knee joint. We propose an automated approach that employs the Swin Transformer to predict the severity of KOA.
  arXiv  Detail & Related papers  (2023-07-10T09:49:30Z)
• Using Deep Learning-based Features Extracted from CT scans to Predict Outcomes in COVID-19 Patients [0.4841303207359715]
  A novel methodology is proposed by combining multi-modal features, extracted from Computed Tomography (CT) scans and Electronic Health Record (EHR) data. Deep learning models are leveraged to extract quantitative features from CT scans. These features combined with those directly read from the EHR database are fed into machine learning models to eventually output the probabilities of patient outcomes.
  arXiv  Detail & Related papers  (2022-05-10T16:22:16Z)
• Predicting Patient Readmission Risk from Medical Text via Knowledge Graph Enhanced Multiview Graph Convolution [67.72545656557858]
  We propose a new method that uses medical text of Electronic Health Records for prediction. We represent discharge summaries of patients with multiview graphs enhanced by an external knowledge graph. Experimental results prove the effectiveness of our method, yielding state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-12-19T01:45:57Z)
• Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration [55.88616573143478]
  Outcome prediction from clinical text can prevent doctors from overlooking possible risks. Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets. We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
  arXiv  Detail & Related papers  (2021-02-08T10:26:44Z)
• Potential Features of ICU Admission in X-ray Images of COVID-19 Patients [8.83608410540057]
  This paper presents an original methodology for extracting semantic features that correlate to severity from a data set with patient ICU admission labels. The methodology employs a neural network trained to recognise lung pathologies to extract the semantic features. The method has shown to be capable of selecting images for the learned features, which could translate some information about their common locations in the lung.
  arXiv  Detail & Related papers  (2020-09-26T13:48:39Z)

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.