A qualitative study of eighteen ICU physicians, nurses, and managers reveals that workflow integration, explainable ...
Please provide your email address to receive an email when new articles are posted on . By training artificial intelligence, or AI, algorithms with electronic health record data, researchers ...
Johns Hopkins University researchers have developed machine-learning (ML) algorithms that can detect the early warning signs of delirium and predict which patients will be at high risk of delirium at ...
New machine learning model predicts cardiac arrest in ICU patients using ECG data with high accuracy
In a recent article published in Npj Digital Medicine, researchers utilized electrocardiogram (ECG) data from a large retrospective cohort to extract various heart rate variability (HRV) measures.
Machine learning prediction of clinical tumor lysis syndrome in critically ill patients with hematologic malignancies using laboratory trajectory features: A MIMIC-IV retrospective cohort study.
Stopping sedation each day may reduce the time people spend on a ventilator, probably reduces the number who die, and probably reduces the time people spend in intensive care and hospital, compared to ...
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