AIMC Topic: Hospital Mortality

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Assessment of acute kidney injury risk using a machine-learning guided generalized structural equation model: a cohort study.

BMC nephrology Feb 22, 2021
BACKGROUND: Acute kidney injury is common in the surgical intensive care unit (ICU). It is associated with poor patient outcomes and high healthcare resource usage. This study's primary objective is to help identify which ICU patients are at high ris...
Retrospective Studies Acute Kidney Injury Female Aged Male Humans Machine Learning Models, Theoretical Mathematical Concepts Middle Aged Risk Assessment Cohort Studies Hospital Mortality
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Machine Learning Identifies Complicated Sepsis Course and Subsequent Mortality Based on 20 Genes in Peripheral Blood Immune Cells at 24 H Post-ICU Admission.

Frontiers in immunology Feb 22, 2021
A complicated clinical course for critically ill patients admitted to the intensive care unit (ICU) usually includes multiorgan dysfunction and subsequent death. Owing to the heterogeneity, complexity, and unpredictability of the disease progression,...
Time Factors Hospital Mortality ROC Curve Computational Biology Databases, Genetic Leukocytes Reproducibility of Results Critical Care Humans Gene Expression Profiling Disease Susceptibility Machine Learning Transcriptome Chromosome Mapping Biomarkers Intensive Care Units Sepsis
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A machine learning approach to identify distinct subgroups of veterans at risk for hospitalization or death using administrative and electronic health record data.

PloS one Feb 19, 2021
BACKGROUND: Identifying individuals at risk for future hospitalization or death has been a major priority of population health management strategies. High-risk individuals are a heterogeneous group, and existing studies describing heterogeneity in hi...
Risk Factors Hospitalization Aged Cross-Sectional Studies Male Middle Aged Humans Veterans Comorbidity Machine Learning Hospital Mortality Databases, Factual United States Female
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Early risk assessment for COVID-19 patients from emergency department data using machine learning.

Scientific reports Feb 18, 2021
Since its emergence in late 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic with more than 55 million reported cases and 1.3 million estimated deaths worldwide. While epidemiological and clinical character...
Humans Machine Learning Pandemics Emergency Service, Hospital Adult United Kingdom Risk Factors Male London COVID-19 Respiration, Artificial Hospitalization Middle Aged Disease Progression Retrospective Studies Aged Risk Assessment Hospitals Female Aged, 80 and over Hospital Mortality SARS-CoV-2 ROC Curve
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Machine learning prediction models for prognosis of critically ill patients after open-heart surgery.

Scientific reports Feb 9, 2021
We aimed to build up multiple machine learning models to predict 30-days mortality, and 3 complications including septic shock, thrombocytopenia, and liver dysfunction after open-heart surgery. Patients who underwent coronary artery bypass surgery, a...
Neural Networks, Computer Machine Learning Databases, Factual Critical Illness Female Humans Hospital Mortality Models, Biological Cardiac Surgical Procedures Disease-Free Survival Heart Diseases Aged Survival Rate Male Middle Aged
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Machine learning-based prediction of in-hospital mortality using admission laboratory data: A retrospective, single-site study using electronic health record data.

PloS one Feb 5, 2021
Risk assessment of in-hospital mortality of patients at the time of hospitalization is necessary for determining the scale of required medical resources for the patient depending on the patient's severity. Because recent machine learning application ...
Databases, Factual Retrospective Studies Electronic Health Records Female ROC Curve Hospitalization Humans Male Diagnostic Tests, Routine Machine Learning Middle Aged Risk Assessment Hospital Mortality
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An Artificial Neural Networks Model for Early Predicting In-Hospital Mortality in Acute Pancreatitis in MIMIC-III.

BioMed research international Jan 28, 2021
BACKGROUND: Early and accurate evaluation of severity and prognosis in acute pancreatitis (AP), especially at the time of admission is very significant. This study was aimed to develop an artificial neural networks (ANN) model for early prediction of...
Humans Hospital Mortality Neural Networks, Computer Pancreatitis Aged Databases, Factual Female Sensitivity and Specificity Male Middle Aged
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A comparison of machine learning models versus clinical evaluation for mortality prediction in patients with sepsis.

PloS one Jan 19, 2021
INTRODUCTION: Patients with sepsis who present to an emergency department (ED) have highly variable underlying disease severity, and can be categorized from low to high risk. Development of a risk stratification tool for these patients is important f...
Retrospective Studies Male Humans Hospital Mortality Female Middle Aged Machine Learning Predictive Value of Tests Risk Factors Severity of Illness Index Emergency Service, Hospital Aged Sepsis Models, Biological Aged, 80 and over
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Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission.

Scientific reports Jan 13, 2021
The aim of this study was to develop a predictive model of pediatric mortality in the early stages of intensive care unit (ICU) admission using machine learning. Patients less than 18 years old who were admitted to ICUs at four tertiary referral hosp...
Intensive Care Units, Pediatric Models, Biological Child Mortality Infant Mortality Patient Admission Infant Humans Child, Preschool Machine Learning Child Retrospective Studies Hospital Mortality Female Male
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Machine Learning Versus Usual Care for Diagnostic and Prognostic Prediction in the Emergency Department: A Systematic Review.

Academic emergency medicine : official journal of the Society for Academic Emergency Medicine Jan 2, 2021
OBJECTIVE: Having shown promise in other medical fields, we sought to determine whether machine learning (ML) models perform better than usual care in diagnostic and prognostic prediction for emergency department (ED) patients.
Triage Emergency Service, Hospital Humans Machine Learning Prognosis Hospital Mortality
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