Hospital Mortality

Prediction of mortality in sepsis patients using stacked ensemble machine learning algorithm.

Journal of postgraduate medicine 39641373

INTRODUCTION: Machine learning (ML) has been tried in predicting outcomes following sepsis. This study aims to identify the utility of stacked ensemble algorithm in predicting mortality.

Adult Aged Algorithms Area Under Curve Cohort Studies Female Hospital Mortality Humans Machine Learning Male Middle Aged Prognosis Sepsis Support Vector Machine

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Machine learning-driven in-hospital mortality prediction in HIV/AIDS patients with infection: a single-centred retrospective study.

Journal of medical microbiology 39606806

() is a widely disseminated betaherpesvirus that typically induces latant infections. In immunocompromised populations, especially transplant and HIV-infected patients, infection increases in-hospital mortality. Although machine learning models ha...

Acquired Immunodeficiency Syndrome Adult China Cytomegalovirus Cytomegalovirus Infections Female HIV Infections Hospital Mortality Humans Machine Learning Male Middle Aged Prognosis Retrospective Studies

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Prognostic value of glycaemic variability for mortality in critically ill atrial fibrillation patients and mortality prediction model using machine learning.

Cardiovascular diabetology 39593120

BACKGROUND: The burden of atrial fibrillation (AF) in the intensive care unit (ICU) remains heavy. Glycaemic control is important in the AF management. Glycaemic variability (GV), an emerging marker of glycaemic control, is associated with unfavourab...

Aged Aged, 80 and over Atrial Fibrillation Biomarkers Blood Glucose Cause of Death Critical Illness Databases, Factual Decision Support Techniques Female Glycemic Control Hospital Mortality Humans Intensive Care Units Machine Learning Male Middle Aged Predictive Value of Tests Prognosis Reproducibility of Results Retrospective Studies Risk Assessment Risk Factors Time Factors

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Predictive model of in-hospital mortality in liver cirrhosis patients with hyponatremia: an artificial neural network approach.

Scientific reports 39567595

Hyponatremia can worsen the outcomes of patients with liver cirrhosis. However, it remains unclear about how to predict the risk of death in cirrhotic patients with hyponatremia. Patients with liver cirrhosis and hyponatremia were screened. Eligible ...

Aged Female Hospital Mortality Humans Hyponatremia Liver Cirrhosis Male Middle Aged Neural Networks, Computer Prognosis ROC Curve Severity of Illness Index

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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 39695656

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...

Aged Biomarkers Blood Glucose Cause of Death China Critical Illness Databases, Factual Female Hospital Mortality Humans Intensive Care Units Length of Stay Machine Learning Male Middle Aged Predictive Value of Tests Prognosis Reproducibility of Results Retrospective Studies Risk Assessment Risk Factors Stroke Time Factors Triglycerides

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Machine Learning-Based Prediction Model for ICU Mortality After Continuous Renal Replacement Therapy Initiation in Children.

Critical care explorations 39688905

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 ...

Acute Kidney Injury Adolescent Child Child, Preschool Continuous Renal Replacement Therapy Critical Illness Female Hospital Mortality Humans Infant Intensive Care Units Machine Learning Male Retrospective Studies ROC Curve

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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 39681171

INTRODUCTION: The risk stratification at admission to the intensive cardiac care unit (ICCU) is crucial and remains challenging.

Aged Coronary Care Units Echocardiography Female Hospital Mortality Humans Intensive Care Units Machine Learning Male Middle Aged Prognosis Prospective Studies Risk Assessment

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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 39655759

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...

Acute Disease Aged Aged, 80 and over Cerebral Hemorrhage Female Hospital Mortality Humans Machine Learning Male Middle Aged Predictive Value of Tests Prognosis Retrospective Studies Risk Assessment Risk Factors ROC Curve Tomography, X-Ray Computed

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Performance of machine learning versus the national early warning score for predicting patient deterioration risk: a single-site study of emergency admissions.

BMJ health & care informatics 39632097

OBJECTIVES: Increasing operational pressures on emergency departments (ED) make it imperative to quickly and accurately identify patients requiring urgent clinical intervention. The widespread adoption of electronic health records (EHR) makes rich fe...

Adult Aged Clinical Deterioration Early Warning Score Electronic Health Records Emergency Service, Hospital Female Hospital Mortality Humans Machine Learning Male Middle Aged Patient Admission Retrospective Studies Risk Assessment Triage United Kingdom

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PROGNOSTIC ACCURACY OF MACHINE LEARNING MODELS FOR IN-HOSPITAL MORTALITY AMONG CHILDREN WITH PHOENIX SEPSIS ADMITTED TO THE PEDIATRIC INTENSIVE CARE UNIT.

Shock (Augusta, Ga.) 39671551

Objective: The Phoenix sepsis criteria define sepsis in children with suspected or confirmed infection who have ≥2 in the Phoenix Sepsis Score. The adoption of the Phoenix sepsis criteria eliminated the Systemic Inflammatory Response Syndrome criteri...

Adolescent Child Child, Preschool Female Hospital Mortality Humans Infant Intensive Care Units, Pediatric Machine Learning Male Prognosis Retrospective Studies Sepsis Shock, Septic

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Explore the latest research on artificial intelligence and machine learning in medicine.

Prediction of mortality in sepsis patients using stacked ensemble machine learning algorithm.

Machine learning-driven in-hospital mortality prediction in HIV/AIDS patients with infection: a single-centred retrospective study.

Prognostic value of glycaemic variability for mortality in critically ill atrial fibrillation patients and mortality prediction model using machine learning.

Predictive model of in-hospital mortality in liver cirrhosis patients with hyponatremia: an artificial neural network approach.

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.

Machine Learning-Based Prediction Model for ICU Mortality After Continuous Renal Replacement Therapy Initiation in Children.

Utility of an Echocardiographic Machine Learning Model to Predict Outcomes in Intensive Cardiac Care Unit Patients.

Machine Learning-Based Prediction for In-Hospital Mortality After Acute Intracerebral Hemorrhage Using Real-World Clinical and Image Data.

Performance of machine learning versus the national early warning score for predicting patient deterioration risk: a single-site study of emergency admissions.

PROGNOSTIC ACCURACY OF MACHINE LEARNING MODELS FOR IN-HOSPITAL MORTALITY AMONG CHILDREN WITH PHOENIX SEPSIS ADMITTED TO THE PEDIATRIC INTENSIVE CARE UNIT.

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