AIMC Topic: Intensive Care Units

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An AIoT enabled system for optimizing data retrieval in the intensive care unit evaluated in a randomized crossover pilot trial.

Scientific reports
Healthcare providers (HCPs) in the intensive care unit (ICU) frequently face information overload, which can result in cognitive fatigue and decision-making errors. This study compares the efficiency and accuracy of data collection between an artific...

Prediction of Moderate-to-Severe Sepsis-Associated Acute Kidney Injury Using a Dual-Timepoint Machine Learning Model: Development, Multiregional Validation, and Clinical Deployment Study.

Journal of medical Internet research
BACKGROUND: Sepsis-associated acute kidney injury (SA-AKI) is a frequent and life-threatening complication in patients in the intensive care unit (ICU), significantly increasing both mortality rates and the risk of chronic kidney dysfunction. However...

Construction and evaluation of prediction model for renal function recovery in acute kidney injury patients undergoing continuous renal replacement therapy based on machine learning algorithms.

Annals of medicine
The primary objective of this study is to employ machine learning (ML) algorithms to develop predictive models for renal function recovery in critically ill patients undergoing continuous renal replacement therapy (CRRT) due to acute kidney injury (...

Harnessing the Power of Technology to Transform Delirium Severity Measurement in the Intensive Care Unit: Protocol for a Prospective Cohort Study.

JMIR research protocols
BACKGROUND: Delirium, an acute brain dysfunction, is a complication in up to 50% of patients in the intensive care unit (ICU). Measuring and mitigating delirium severity can reduce associated morbidity and improve long-term health outcomes post disch...

Mortality prediction for ICU patients with mental disorders using large language models ensemble and unstructured medical notes.

PloS one
Assessing mortality risk in the intensive care unit (ICU) is crucial for improving clinical outcomes and management strategies. Conventional artificial intelligence studies often neglect vital clinical information contained in unstructured medical no...

Transposing intensive care innovation from modern warfare to other resource-limited settings.

European journal of trauma and emergency surgery : official publication of the European Trauma Society
BACKGROUND: Delivering intensive care in conflict zones and other resource-limited settings presents unique clinical, logistical, and ethical challenges. These contexts, characterized by disrupted infrastructure, limited personnel, and prolonged fiel...

Explainable mortality prediction models incorporating social health determinants and physical frailty for heart failure patients.

PloS one
There is limited evidence on how social determinants of health (SDOH) and physical frailty (PF) influence mortality prediction in heart failure (HF), particularly for in-hospital, 90-day, and 1-year outcomes. This study aims to develop explainable ma...

Association between hemoglobin glycation index and 28-day all-cause mortality in acute myocardial infarction patients: Analysis of the MIMIC-IV database.

PloS one
Acute myocardial infarction (AMI) substantially fuels the worldwide escalation in both morbidity and mortality. The hemoglobin glycation index (HGI) is linked to a range of undesirable outcomes, but its relationship with short-term outcomes in AMI pa...

Integrated Microbiome Data Analysis Reveals Potential Pneumonia Microbial Biomarkers in ICU Patients: A Machine Learning Approach.

Current microbiology
The human microbiome is pivotal in maintaining health and managing diseases. By examining the core microbiome in intensive care units (ICU) patients with pneumonia, we can gain valuable insights into the microbial communities associated with disease ...

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

PloS one
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...