AIMC Topic: Intensive Care Units

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Harnessing AI in critical care: opportunities, challenges and key steps for success.

Thorax
BACKGROUND: The integration of artificial intelligence (AI) into critical care offers significant potential to enhance early diagnosis, predict patient deterioration, personalise treatment and inform clinical decision-making. Despite this promise, AI...

Association between stress hyperglycemia ratio and all-cause mortality in critically ill patients with mitral valve disease.

Scientific reports
The study of stress hyperglycemia ratio (SHR) aims to further investigate the relationship between chronic glucose factors and adverse clinical events, particularly cardiovascular outcomes, in critically ill patients. However, prior research has not ...

Association of albumin-bilirubin grade with prognosis in ICU patients with pulmonary edema: a retrospective cohort study and a predictive model based on machine learning.

BMC pulmonary medicine
BACKGROUND: The Albumin-Bilirubin (ALBI) grade was initially used to assess liver reserve function in patients with cirrhosis and has since been applied in the prognostic evaluation of various diseases. This study explored the relationship between th...

Early prediction of vasopressor initiation in ICU sepsis patients using an interpretable EHR-based ML model.

BMC medical informatics and decision making
BACKGROUND: Early identification of septic patients who will require vasopressor support could provide a critical window for hemodynamic optimisation, yet current bedside cues often appear only when shock is imminent.

Continuous Physiologic Markers of Heart Rate Variability Derived From Bedside Electrocardiogram Precede Onset of Acute Respiratory Distress Syndrome: A Physiologic Modeling Study.

Critical care explorations
OBJECTIVE: Acute respiratory distress syndrome (ARDS) is estimated to be prevalent in 10% of ICU patients and results in high mortality rates of up to 45%. The recognition of ARDS can be complex and is often delayed or missed entirely. Recognition of...

A machine learning model for predicting 28-day mortality in ICU patients with community-acquired pneumonia and acute kidney injury.

Scientific reports
Acute kidney injury is a common and critical complication in patients with community-acquired pneumonia who are admitted to intensive care units, substantially increasing their risk of short-term mortality. To enhance early clinical decision-making, ...

COVID-19 severity analysis for clinical decision support based on machine learning approach.

Scientific reports
The COVID-19 pandemic has placed immense pressure on global healthcare systems, underscoring the urgent need for early and accurate prediction of disease severity to improve patient care and optimize resource allocation. Failure in ward allocation ca...

Machine learning model of clinical laboratory data for 30-day mortality of patients with hodgkin's lymphoma in ICU: a retrospective study based on MIMIC-IV database.

Clinical and experimental medicine
Prognostic stratification of Hodgkin lymphoma (HL) patients in ICU remains challenging, with conventional scoring systems often overlooking pathophysiological biomarkers. This retrospective cohort study analyzed 1,908 HL patients from the MIMIC-IV da...

Upper-limb rehabilitation interventions delivered by healthcare professionals for adult patients in the intensive care unit setting: protocol for a scoping review.

BMJ open
INTRODUCTION: Post-intensive care syndrome affects up to 70% of adult intensive care unit (ICU) survivors, with ICU-acquired weakness contributing substantially to long-term disability. Despite evidence supporting early and structured rehabilitation ...

Unsupervised Characterization of Temporal Dataset Shifts as an Early Indicator of AI Performance Variations: Evaluation Study Using the Medical Information Mart for Intensive Care-IV Dataset.

JMIR medical informatics
BACKGROUND: Reusing long-term data from electronic health records is essential for training reliable and effective health artificial intelligence (AI). However, intrinsic changes in health data distributions over time-known as dataset shifts, which i...