AIMC Topic: Hospital Mortality
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Comparison of machine learning and nomogram to predict 30-day in-hospital mortality in patients with acute myocardial infarction combined with cardiogenic shock: a retrospective study based on the eICU-CRD and MIMIC-IV databases.
BMC cardiovascular disorders
Mar 19, 2025
BACKGROUND: To evaluate the predictive utility of machine learning and nomogram in predicting in-hospital mortality in patients with acute myocardial infarction complicated by cardiogenic shock (AMI-CS), and to visualize the model results in order to...
Development and validation of machine learning-based prediction model for outcome of cardiac arrest in intensive care units.
Scientific reports
Mar 13, 2025
Cardiac arrest (CA) poses a significant global health challenge and often results in poor prognosis. We developed an interpretable and applicable machine learning (ML) model for predicting in-hospital mortality of CA patients who survived more than 7...
Development and validation of an interpretable machine learning model for predicting in-hospital mortality for ischemic stroke patients in ICU.
International journal of medical informatics
Mar 9, 2025
BACKGROUND: Timely and accurate outcome prediction is essential for clinical decision-making for ischemic stroke patients in the intensive care unit (ICU). However, the interpretation and translation of predictive models into clinical applications ar...
Predictive value of machine learning for in-hospital mortality risk in acute myocardial infarction: A systematic review and meta-analysis.
International journal of medical informatics
Mar 8, 2025
BACKGROUND: Machine learning (ML) models have been constructed to predict the risk of in-hospital mortality in patients with myocardial infarction (MI). Due to diverse ML models and modeling variables, along with the significant imbalance in data, th...
Explainable machine learning model for predicting acute pancreatitis mortality in the intensive care unit.
BMC gastroenterology
Mar 3, 2025
BACKGROUND: Current prediction models are suboptimal for determining mortality risk in patients with acute pancreatitis (AP); this might be improved by using a machine learning (ML) model. In this study, we aimed to construct an explainable ML model ...
Risk factors and an interpretability tool of in-hospital mortality in critically ill patients with acute myocardial infarction.
Clinical medicine (London, England)
Feb 27, 2025
OBJECTIVE: We aim to develop and validate an interpretable machine-learning model that can provide critical information for the clinical treatment of critically ill patients with acute myocardial infarction (AMI).
Machine Learning-Based Mortality Prediction for Acute Gastrointestinal Bleeding Patients Admitted to Intensive Care Unit.
Current medical science
Feb 27, 2025
OBJECTIVE: The study aimed to develop machine learning (ML) models to predict the mortality of patients with acute gastrointestinal bleeding (AGIB) in the intensive care unit (ICU) and compared their prognostic performance with that of Acute Physiolo...
Machine learning based on nutritional assessment to predict adverse events in older inpatients with possible sarcopenia.
Aging clinical and experimental research
Feb 22, 2025
BACKGROUND: The accuracy of current tools for predicting adverse events in older inpatients with possible sarcopenia is still insufficient to develop individualized nutrition-related management strategies. The objectives were to develop a machine lea...
New machine-learning models outperform conventional risk assessment tools in Gastrointestinal bleeding.
Scientific reports
Feb 21, 2025
Rapid and accurate identification of high-risk acute gastrointestinal bleeding (GIB) patients is essential. We developed two machine-learning (ML) models to calculate the risk of in-hospital mortality in patients admitted due to overt GIB. We analyze...
Early prediction of 30-day mortality in patients with surgical wound infections following cardiothoracic surgery: Development and validation of the SWICS-30 score utilizing conventional logistic regression and artificial neural network.
The Brazilian journal of infectious diseases : an official publication of the Brazilian Society of Infectious Diseases
Feb 21, 2025
INTRODUCTION: We aimed to create and validate the 30-day prognostic score for mortality in patients with surgical wound infection (SWICS-30) after cardiothoracic surgery.
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