AIMC Topic: Hospital Mortality

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External validation of an artificial intelligence model using clinical variables, including ICD-10 codes, for predicting in-hospital mortality among trauma patients: a multicenter retrospective cohort study.

Scientific reports Jan 7, 2025
Artificial intelligence (AI) is being increasingly applied in healthcare to improve patient care and clinical outcomes. We previously developed an AI model using ICD-10 (International Classification of Diseases, Tenth Revision) codes with other clini...
Humans Artificial Intelligence Databases, Factual Male Middle Aged Cohort Studies International Classification of Diseases Hospital Mortality Wounds and Injuries ROC Curve Retrospective Studies Female Aged Adult
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An interpretable machine learning model for predicting in-hospital mortality in ICU patients with ventilator-associated pneumonia.

PloS one Jan 7, 2025
BACKGROUND: Ventilator-associated pneumonia (VAP) is a common nosocomial infection in ICU, significantly associated with poor outcomes. However, there is currently a lack of reliable and interpretable tools for assessing the risk of in-hospital morta...
Hospital Mortality ROC Curve Humans Pneumonia, Ventilator-Associated Machine Learning Intensive Care Units Female Aged Male Middle Aged
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Employing a low-code machine learning approach to predict in-hospital mortality and length of stay in patients with community-acquired pneumonia.

Scientific reports Jan 2, 2025
Community-acquired pneumonia (CAP) is associated with high mortality rates and often results in prolonged hospital stays. The potential of machine learning to enhance prediction accuracy in this context is significant, yet clinicians often lack the p...
Machine Learning Retrospective Studies Female Aged Aged, 80 and over Humans ROC Curve Pneumonia Area Under Curve Data Mining Community-Acquired Infections Male Middle Aged Length of Stay Hospital Mortality
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Mortality prediction of inpatients with NSTEMI in a premier hospital in China based on stacking model.

PloS one Dec 31, 2024
BACKGROUND: Acute myocardial infarction (AMI) remains a leading cause of hospitalization and death in China. Accurate mortality prediction of inpatient is crucial for clinical decision-making of non-ST-segment elevation myocardial infarction (NSTEMI)...
Hospital Mortality Logistic Models Support Vector Machine Aged Non-ST Elevated Myocardial Infarction Humans Inpatients Male Algorithms Decision Trees Middle Aged Female China Hospitalization
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Robust multi-modal fusion architecture for medical data with knowledge distillation.

Computer methods and programs in biomedicine Dec 18, 2024
BACKGROUND: The fusion of multi-modal data has been shown to significantly enhance the performance of deep learning models, particularly on medical data. However, missing modalities are common in medical data due to patient specificity, which poses a...
Algorithms Deep Learning Hospital Mortality Humans ROC Curve Area Under Curve
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Exploring the prognostic impact of triglyceride-glucose index in critically ill patients with first-ever stroke: insights from traditional methods and machine learning-based mortality prediction.

Cardiovascular diabetology Dec 18, 2024
BACKGROUND: The incidence and mortality of first-ever strokes have risen sharply, especially in the intensive care unit (ICU). Emerging surrogate for insulin resistance, triglyceride-glucose index (TyG), has been linked to stroke prognosis. We aims t...
Reproducibility of Results Risk Factors Aged Male Middle Aged Risk Assessment China Length of Stay Blood Glucose Prognosis Retrospective Studies Critical Illness Intensive Care Units Female Humans Machine Learning Databases, Factual Biomarkers Time Factors Hospital Mortality Predictive Value of Tests Stroke Cause of Death Triglycerides
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Machine Learning-Based Prediction Model for ICU Mortality After Continuous Renal Replacement Therapy Initiation in Children.

Critical care explorations Dec 17, 2024
BACKGROUND: Continuous renal replacement therapy (CRRT) is the favored renal replacement therapy in critically ill patients. Predicting clinical outcomes for CRRT patients is difficult due to population heterogeneity, varying clinical practices, and ...
Child Continuous Renal Replacement Therapy Hospital Mortality Acute Kidney Injury ROC Curve Humans Critical Illness Machine Learning Intensive Care Units Female Retrospective Studies Male Infant Adolescent Child, Preschool
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Machine Learning for In-hospital Mortality Prediction in Critically Ill Patients With Acute Heart Failure: A Retrospective Analysis Based on the MIMIC-IV Database.

Journal of cardiothoracic and vascular anesthesia Dec 16, 2024
BACKGROUND: The incidence, mortality, and readmission rates for acute heart failure (AHF) are high, and the in-hospital mortality for AHF patients in the intensive care unit (ICU) is higher. However, there is currently no method to accurately predict...
Aged Aged, 80 and over Male Middle Aged Hospital Mortality Retrospective Studies Critical Illness Intensive Care Units Humans Female Predictive Value of Tests Machine Learning Acute Disease Databases, Factual Heart Failure
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Utility of an Echocardiographic Machine Learning Model to Predict Outcomes in Intensive Cardiac Care Unit Patients.

Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography Dec 14, 2024
INTRODUCTION: The risk stratification at admission to the intensive cardiac care unit (ICCU) is crucial and remains challenging.
Aged Male Echocardiography Middle Aged Risk Assessment Hospital Mortality Coronary Care Units Machine Learning Prospective Studies Prognosis Intensive Care Units Female Humans
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Machine Learning-Based Prediction for In-Hospital Mortality After Acute Intracerebral Hemorrhage Using Real-World Clinical and Image Data.

Journal of the American Heart Association Dec 10, 2024
BACKGROUND: Machine learning (ML) techniques are widely employed across various domains to achieve accurate predictions. This study assessed the effectiveness of ML in predicting early mortality risk among patients with acute intracerebral hemorrhage...
Risk Factors Aged Aged, 80 and over Male Middle Aged Prognosis Retrospective Studies Tomography, X-Ray Computed Female Predictive Value of Tests Acute Disease Risk Assessment Hospital Mortality ROC Curve Cerebral Hemorrhage Humans Machine Learning
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