AIMC Topic: Intensive Care Units

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Predicting in-hospital mortality in ICU patients with lymphoma using machine learning models.

PloS one Aug 20, 2025
BACKGROUND: Lymphoma is a severe condition with high mortality rates, often requiring ICU admission. Traditional risk stratification tools like SOFA and APACHE scores struggle to capture complex clinical interactions. Machine learning (ML) models off...
Retrospective Studies Intensive Care Units Female Risk Factors Humans Machine Learning Middle Aged Hospital Mortality ROC Curve Lymphoma Aged Adult Male
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Real-time prediction of intensive care unit patient acuity and therapy requirements using state-space modelling.

Nature communications Aug 8, 2025
Intensive care unit (ICU) patients often experience rapid changes in clinical status, requiring timely identification of deterioration to guide life-sustaining interventions. Current artificial intelligence (AI) models for acuity assessment rely on m...
Critical Care Humans Artificial Intelligence Intensive Care Units Hospital Mortality Patient Acuity
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Machine learning algorithms to predict the risk of admission to intensive care units in HIV-infected individuals: a single-centre study.

Virology journal Aug 5, 2025
Antiretroviral therapy (ART) has transformed HIV from a rapidly progressive and fatal disease to a chronic disease with limited impact on life expectancy. However, people living with HIV(PLWHs) faced high critical illness risk due to the increased pr...
Neural Networks, Computer Machine Learning Algorithms Intensive Care Units Humans HIV Infections ROC Curve Adult Male Middle Aged Risk Assessment Female Risk Factors Hospitalization
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The relationship between clinical subtypes, prognosis, and treatment in ICU patients with acute cholangitis using unsupervised machine learning methods.

BMC infectious diseases Aug 4, 2025
BACKGROUND: Acute cholangitis (AC) presents with significant clinical heterogeneity, and existing severity classifications have limited prognostic value in critically ill patients. Subtypes of AC in critically ill patients have not been investigated.
Aged Male Middle Aged Unsupervised Machine Learning Critical Illness Intensive Care Units Female Acute Disease Cholangitis Humans Prognosis Retrospective Studies
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Explainable machine learning for predicting ICU mortality in myocardial infarction patients using pseudo-dynamic data.

Scientific reports Jul 31, 2025
Myocardial infarction (MI) remains one of the greatest contributors to mortality, and patients admitted to the intensive care unit (ICU) with myocardial infarction are at higher risk of death. In this study, we use two retrospective cohorts extracted...
Male Middle Aged Hospital Mortality Myocardial Infarction Retrospective Studies Intensive Care Units Female Aged Machine Learning Algorithms Databases, Factual Humans
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Predicting the clinical evolution of septic patients from routinely collected data and vital signs variability using machine learning.

Physiological measurement Jul 30, 2025
The existing literature lacks a comprehensive analysis of the clinical evolution of septic patients, which is highly heterogeneous and patient-dependent. The aim of this study is to develop machine learning models capable of predicting the clinical e...
Sepsis Vital Signs Humans Female Machine Learning Aged Intensive Care Units Male Middle Aged
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Trends and methods in intensive care unit (ICU) research using machine learning: latent dirichlet allocation (LDA)-based thematic literature review.

BMC medical informatics and decision making Jul 29, 2025
INTRODUCTION: The use of machine learning (ML) in intensive care units (ICUs) has led to a large yet fragmented body of literature. It is imperative to conduct a systematic analysis and synthesis of this research to identify methodological trends, cl...
Humans Machine Learning Intensive Care Units Biomedical Research Bayes Theorem
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Developing and validating machine learning models to predict next-day extubation.

Scientific reports Jul 29, 2025
Criteria to identify patients who are ready to be liberated from mechanical ventilation (MV) are imprecise, often resulting in prolonged MV or reintubation, both of which are associated with adverse outcomes. Daily protocol-driven assessment of the n...
Aged Machine Learning Respiration, Artificial Humans Airway Extubation Male Middle Aged Ventilator Weaning Prospective Studies Intensive Care Units Female
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Identifying key physiological and clinical factors for traumatic brain injury patient management using network analysis and machine learning.

PloS one Jul 28, 2025
In the intensive care unit (ICU), managing traumatic brain injury (TBI) patients presents significant challenges due to the dynamic interaction between physiological and clinical markers. This study aims to uncover these subtle interconnections and i...
Brain Injuries, Traumatic Blood Pressure Heart Rate Humans Glasgow Coma Scale Neural Networks, Computer Machine Learning Adult Intensive Care Units Male Middle Aged Female Electrocardiography Aged Young Adult
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Development and validation of machine learning-based risk prediction models for ICU-acquired weakness: a prospective cohort study.

European journal of medical research Jul 24, 2025
BACKGROUND: Intensive care unit (ICU)-acquired weakness (ICUAW) is a prevalent complication in critically ill patients, marked by symmetrical respiratory and limb muscle weakness, which adversely affects long-term outcomes. Early identification of hi...
Risk Factors Aged Male Middle Aged Critical Illness Humans Intensive Care Units Female Machine Learning Risk Assessment Algorithms ROC Curve Prospective Studies Muscle Weakness Prognosis
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