AIMC Topic: Intensive Care Units

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Fast and interpretable mortality risk scores for critical care patients.

Journal of the American Medical Informatics Association : JAMIA Apr 1, 2025
OBJECTIVE: Prediction of mortality in intensive care unit (ICU) patients typically relies on black box models (that are unacceptable for use in hospitals) or hand-tuned interpretable models (that might lead to the loss in performance). We aim to brid...
Humans Machine Learning Hospital Mortality Intensive Care Units Algorithms Critical Care Risk Assessment Critical Illness
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[Establishing of mortality predictive model for elderly critically ill patients using simple bedside indicators and interpretable machine learning algorithms].

Zhonghua wei zhong bing ji jiu yi xue Feb 1, 2025
OBJECTIVE: To explore the feasibility of incorporating simple bedside indicators into death predictive model for elderly critically ill patients based on interpretability machine learning algorithms, providing a new scheme for clinical disease assess...
Humans Machine Learning Critical Illness Intensive Care Units Female Aged Algorithms Prognosis Retrospective Studies Organ Dysfunction Scores Male Hospital Mortality APACHE
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Scale to predict risk for refractory septic shock based on a hybrid approach using machine learning and regression modeling.

Emergencias : revista de la Sociedad Espanola de Medicina de Emergencias Feb 1, 2025
OBJECTIVE: To develop a scale to predict refractory septic shock (SS) based on clinical variables recorded during initial evaluations of patients.
Emergency Service, Hospital Registries Norepinephrine ROC Curve Shock, Septic Regression Analysis Retrospective Studies Intensive Care Units Female Middle Aged Risk Assessment Aged Male Humans Machine Learning
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[Predicting Intensive Care Unit Mortality in Patients With Heart Failure Combined With Acute Kidney Injury Using an Interpretable Machine Learning Model: A Retrospective Cohort Study].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition Jan 20, 2025
OBJECTIVE: Heart failure (HF) complicated by acute kidney injury (AKI) significantly impacts patient outcomes, and it is crucial to make early predictions of short-term mortality. This study is focused on developing an interpretable machine learning ...
Hospital Mortality Heart Failure Retrospective Studies Acute Kidney Injury Intensive Care Units Humans Machine Learning Male Female Middle Aged Prognosis Aged
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Individualized multi-treatment response curves estimation using RBF-net with shared neurons.

Biometrics Jan 7, 2025
Heterogeneous treatment effect estimation is an important problem in precision medicine. Specific interests lie in identifying the differential effect of different treatments based on some external covariates. We propose a novel non-parametric treatm...
Biometry Statistics, Nonparametric Markov Chains Organ Dysfunction Scores Monte Carlo Method Precision Medicine Intensive Care Units Length of Stay Models, Statistical Bayes Theorem Sepsis Treatment Outcome Neural Networks, Computer Algorithms Computer Simulation Humans
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Explainable Artificial Intelligence for Early Prediction of Pressure Injury Risk.

American journal of critical care : an official publication, American Association of Critical-Care Nurses Sep 1, 2024
BACKGROUND: Hospital-acquired pressure injuries (HAPIs) have a major impact on patient outcomes in intensive care units (ICUs). Effective prevention relies on early and accurate risk assessment. Traditional risk-assessment tools, such as the Braden S...
ROC Curve Pressure Ulcer Artificial Intelligence Male Machine Learning Middle Aged Humans Algorithms Risk Assessment Female Intensive Care Units Risk Factors Aged
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Optimize individualized energy delivery for septic patients using predictive deep learning models.

Asia Pacific journal of clinical nutrition Sep 1, 2024
BACKGROUND AND OBJECTIVES: We aim to establish deep learning models to optimize the individualized energy delivery for septic patients.
Humans Aged Male Intensive Care Units Sepsis Female Middle Aged Energy Intake Deep Learning
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Optimizing ICU Care: Machine Learning and PCA for Early Prediction of Renal Replacement Therapy Requirement.

Studies in health technology and informatics Aug 22, 2024
Forecasting the need for Renal Replacement Therapy (RRT) in intensive care units (ICUs) at an early stage can enhance patient outcomes and optimize resource allocation. The study aimed to develop a model for early prediction of Renal Replacement Ther...
Critical Care Humans Intensive Care Units Machine Learning Male Renal Replacement Therapy Principal Component Analysis
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A Comparative Analysis of Federated and Centralized Learning for SpO2 Prediction in Five Critical Care Databases.

Studies in health technology and informatics Aug 22, 2024
This study explores the potential of federated learning (FL) to develop a predictive model of hypoxemia in intensive care unit (ICU) patients. Centralized learning (CL) and local learning (LL) approaches have been limited by the localized nature of d...
Humans Hypoxia Electronic Health Records Machine Learning Critical Care Databases, Factual Oximetry Oxygen Intensive Care Units
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User-Centered Development of Explanation User Interfaces for AI-Based CDSS: Lessons Learned from Early Phases.

Studies in health technology and informatics Aug 22, 2024
This paper reports lessons learned during the early phases of the user-centered design process for an explanation user interface for an AI-based clinical decision support system for the intensive care unit. This paper focuses on identifying and verif...
Humans Artificial Intelligence Intensive Care Units User-Computer Interface Decision Support Systems, Clinical User-Centered Design
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