AIMC Topic: Hospital Mortality

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Machine learning models to evaluate mortality in pediatric patients with pneumonia in the intensive care unit.

Pediatric pulmonology Feb 14, 2024
OBJECTIVES: This study aimed to predict mortality in children with pneumonia who were admitted to the intensive care unit (ICU) to aid decision-making.
Pneumonia Intensive Care Units, Pediatric Male ROC Curve Infant Taiwan Adolescent Child, Preschool Retrospective Studies Child Humans Intensive Care Units Hospital Mortality Machine Learning Female
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A comparative study of explainable ensemble learning and logistic regression for predicting in-hospital mortality in the emergency department.

Scientific reports Feb 10, 2024
This study addresses the challenges associated with emergency department (ED) overcrowding and emphasizes the need for efficient risk stratification tools to identify high-risk patients for early intervention. While several scoring systems, often bas...
Humans Machine Learning Hospital Mortality Logistic Models Emergency Service, Hospital Retrospective Studies Adult Cross-Sectional Studies
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Heart rate complexity helps mortality prediction in the intensive care unit: A pilot study using artificial intelligence.

Computers in biology and medicine Jan 3, 2024
BACKGROUND: In intensive care units (ICUs), accurate mortality prediction is crucial for effective patient management and resource allocation. The Simplified Acute Physiology Score II (SAPS-2), though commonly used, relies heavily on comprehensive cl...
Hospital Mortality ROC Curve Heart Rate Artificial Intelligence Intensive Care Units Pilot Projects Humans
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Deep learning-based prediction of in-hospital mortality for sepsis.

Scientific reports Jan 3, 2024
As a serious blood infection disease, sepsis is characterized by a high mortality risk and many complications. Accurate assessment of mortality risk of patients with sepsis can help physicians in Intensive Care Unit make optimal clinical decisions, w...
Deep Learning Sepsis Hospital Mortality Humans Machine Learning Intensive Care Units ROC Curve
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Congenital Heart Surgery Machine Learning-Derived In-Depth Benchmarking Tool.

The Annals of thoracic surgery Dec 6, 2023
BACKGROUND: We previously showed that machine learning-based methodologies of optimal classification trees (OCTs) can accurately predict risk after congenital heart surgery and assess case-mix-adjusted performance after benchmark procedures. We exten...
Humans Machine Learning Databases, Factual Retrospective Studies Female Male Benchmarking Risk Assessment Cardiac Surgical Procedures Hospital Mortality Heart Defects, Congenital Europe
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Novel Preoperative Risk Stratification Using Digital Phenotyping Applying a Scalable Machine-Learning Approach.

Anesthesia and analgesia Dec 5, 2023
BACKGROUND: Classification of perioperative risk is important for patient care, resource allocation, and guiding shared decision-making. Using discriminative features from the electronic health record (EHR), machine-learning algorithms can create dig...
Risk Assessment Postoperative Complications Length of Stay Hospital Mortality Predictive Value of Tests Adult Male Middle Aged Risk Factors Aged Humans Machine Learning Phenotype Retrospective Studies Electronic Health Records Female
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An interpretable ensemble learning model facilitates early risk stratification of ischemic stroke in intensive care unit: Development and external validation of ICU-ISPM.

Computers in biology and medicine Oct 11, 2023
Ischemic stroke (IS) is a common and severe condition that requires intensive care unit (ICU) admission, with high mortality and variable prognosis. Accurate and reliable predictive tools that enable early risk stratification can facilitate intervent...
Risk Assessment Hospital Mortality Ischemic Stroke Male Intensive Care Units Middle Aged Humans Female Aged Machine Learning Databases, Factual
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A novel neural network for improved in-hospital mortality prediction with irregular and incomplete multivariate data.

Neural networks : the official journal of the International Neural Network Society Aug 12, 2023
Accurate estimation of in-hospital mortality based on patients' physiological time series data improves the performance of the clinical decision support systems and assists hospital providers in allocating resources. In practice, the data quality iss...
Electronic Health Records Time Factors Hospital Mortality Humans Neural Networks, Computer
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Using machine learning to estimate health spillover effects.

The European journal of health economics : HEPAC : health economics in prevention and care Aug 6, 2023
We develop a nonparametric model to study health spillover effects of policy interventions. We use double/debiased machine learning to estimate the model using data from 74 hospitals in Rio de Janeiro, Brazil, and examine cross-patient spillover effe...
Brazil Length of Stay Hospital Mortality COVID-19 Humans Intensive Care Units Machine Learning SARS-CoV-2 Pandemics
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LGTRL-DE: Local and Global Temporal Representation Learning with Demographic Embedding for in-hospital mortality prediction.

Journal of biomedical informatics Jun 7, 2023
Predicting the patient's in-hospital mortality from the historical Electronic Medical Records (EMRs) can assist physicians to make clinical decisions and assign medical resources. In recent years, researchers proposed many deep learning methods to pr...
Hospital Mortality Humans Neural Networks, Computer
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