Early Prediction of In-Hospital ICU Mortality Using Innovative First-Day Data: A Review

• URL: http://arxiv.org/abs/2505.12344v1
• Date: Sun, 18 May 2025 10:17:31 GMT
• Title: Early Prediction of In-Hospital ICU Mortality Using Innovative First-Day Data: A Review
• Authors: Han Wang,
• Abstract summary: Early and accurate prediction of in-hospital mortality within the first 24 hours of ICU admission is crucial.<n>Traditional scoring systems, while useful, often have limitations in predictive accuracy and adaptability.
• Score: 5.224380092904516
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The intensive care unit (ICU) manages critically ill patients, many of whom face a high risk of mortality. Early and accurate prediction of in-hospital mortality within the first 24 hours of ICU admission is crucial for timely clinical interventions, resource optimization, and improved patient outcomes. Traditional scoring systems, while useful, often have limitations in predictive accuracy and adaptability. Objective: This review aims to systematically evaluate and benchmark innovative methodologies that leverage data available within the first day of ICU admission for predicting in-hospital mortality. We focus on advancements in machine learning, novel biomarker applications, and the integration of diverse data types.

Related papers

• Feature-Enhanced Machine Learning for All-Cause Mortality Prediction in Healthcare Data [0.0]
  This study evaluates machine learning models for all-cause in-hospital mortality prediction using the MIMIC-III database.<n>We extracted key features such as vital signs (e.g., heart rate, blood pressure), laboratory results and demographic information.<n>The Random Forest model achieved the highest performance with an AUC of 0.94, significantly outperforming other machine learning and deep learning approaches.
  arXiv  Detail & Related papers  (2025-03-27T08:04:42Z)
• 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)
• XMI-ICU: Explainable Machine Learning Model for Pseudo-Dynamic Prediction of Mortality in the ICU for Heart Attack Patients [3.5475382876263915]
  Heart attack is one of the greatest contributors to mortality in the United States and globally. We develop a novel pseudo-dynamic machine learning framework for mortality prediction in the ICU with interpretability and clinical risk analysis.
  arXiv  Detail & Related papers  (2023-05-10T12:53:18Z)
• Early prediction of the risk of ICU mortality with Deep Federated Learning [0.0]
  We evaluate the ability of deep Federated Learning to predict the risk of Intensive Care Unit mortality at an early stage. We show that the prediction performance is higher when the patient history window is closer to discharge or death.
  arXiv  Detail & Related papers  (2022-12-01T15:01:27Z)
• 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)
• Optimal discharge of patients from intensive care via a data-driven policy learning framework [58.720142291102135]
  It is important that the patient discharge task addresses the nuanced trade-off between decreasing a patient's length of stay and the risk of readmission or even death following the discharge decision. This work introduces an end-to-end general framework for capturing this trade-off to recommend optimal discharge timing decisions. A data-driven approach is used to derive a parsimonious, discrete state space representation that captures a patient's physiological condition.
  arXiv  Detail & Related papers  (2021-12-17T04:39:33Z)
• 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)
• Prediction of the onset of cardiovascular diseases from electronic health records using multi-task gated recurrent units [51.14334174570822]
  We propose a multi-task recurrent neural network with attention mechanism for predicting cardiovascular events from electronic health records. The proposed approach is compared to a standard clinical risk predictor (QRISK) and machine learning alternatives using 5-year data from a NHS Foundation Trust.
  arXiv  Detail & Related papers  (2020-07-16T17:43:13Z)
• ISeeU2: Visually Interpretable ICU mortality prediction using deep learning and free-text medical notes [0.0]
  We show a Deep Learning model trained on MIMIC-III to predict mortality using raw nursing notes, together with visual explanations for word importance. Our model reaches a ROC of 0.8629, outperforming the traditional SAPS-II score and providing enhanced interpretability when compared with similar Deep Learning approaches.
  arXiv  Detail & Related papers  (2020-05-19T08:30:34Z)
• Integrating Physiological Time Series and Clinical Notes with Deep Learning for Improved ICU Mortality Prediction [21.919977518774015]
  We study how physiological time series data and clinical notes can be integrated into a unified mortality prediction model. Our results show that a late fusion approach can provide a statistically significant improvement in prediction mortality over using individual modalities in isolation.
  arXiv  Detail & Related papers  (2020-03-24T18:25:49Z)

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.