AIMC Topic: Critical Illness

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Role of artificial intelligence in critical care nutrition support and research.

Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition Jul 28, 2024
Nutrition plays a key role in the comprehensive care of critically ill patients. Determining optimal nutrition strategy, however, remains a subject of intense debate. Artificial intelligence (AI) applications are becoming increasingly common in medic...
Enteral Nutrition Nutritional Support Intensive Care Units Nutrition Assessment Malnutrition Critical Care Humans Artificial Intelligence Critical Illness
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Machine learning for predicting mortality in adult critically ill patients with Sepsis: A systematic review.

Journal of critical care Jul 25, 2024
INTRODUCTION: Various Machine Learning (ML) models have been used to predict sepsis-associated mortality. We conducted a systematic review to evaluate the methodologies employed in studies to predict mortality among patients with sepsis.
Humans Machine Learning Critical Illness Adult Sepsis
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Creatinine clearance in critically ill adults: prospective comparison of prediction by intensive care unit physicians and machine learning models.

Intensive care medicine Jul 24, 2024
Machine Learning Prospective Studies Acute Kidney Injury Critical Illness Humans Male Intensive Care Units Middle Aged Female Creatinine Aged Adult
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Retrospective analysis of interpretable machine learning in predicting ICU thrombocytopenia in geriatric ICU patients.

Scientific reports Jul 20, 2024
We developed an interpretable machine learning algorithm that prospectively predicts the risk of thrombocytopenia in older critically ill patients during their stay in the intensive care unit (ICU), ultimately aiding clinical decision-making and impr...
Aged, 80 and over Male ROC Curve Thrombocytopenia Machine Learning Algorithms Retrospective Studies Humans Intensive Care Units Critical Illness Clinical Decision-Making Female Aged
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A proof of concept for microcirculation monitoring using machine learning based hyperspectral imaging in critically ill patients: a monocentric observational study.

Critical care (London, England) Jul 10, 2024
BACKGROUND: Impaired microcirculation is a cornerstone of sepsis development and leads to reduced tissue oxygenation, influenced by fluid and catecholamine administration during treatment. Hyperspectral imaging (HSI) is a non-invasive bedside technol...
Humans Machine Learning Critical Illness Female Sepsis Monitoring, Physiologic Hyperspectral Imaging Aged Proof of Concept Study Adult Microcirculation Male Middle Aged
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Patient stratification based on the risk of severe illness in emergency departments through collaborative machine learning models.

The American journal of emergency medicine Jun 15, 2024
OBJECTIVES: Emergency department (ED) overcrowding presents a global challenge that inhibits prompt care for critically ill patients. Traditional 5-level triage system that heavily rely on the judgment of the triage staff could fail to detect subtle ...
Humans Neural Networks, Computer Risk Assessment Severity of Illness Index ROC Curve Aged Taiwan Machine Learning Triage Adult Emergency Service, Hospital Male Retrospective Studies Middle Aged Critical Illness Female
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Towards the automatic calculation of the EQUAL Candida Score: Extraction of CVC-related information from EMRs of critically ill patients with candidemia in Intensive Care Units.

Journal of biomedical informatics Jun 5, 2024
OBJECTIVES: Candidemia is the most frequent invasive fungal disease and the fourth most frequent bloodstream infection in hospitalized patients. Its optimal management is crucial for improving patients' survival. The quality of candidemia management ...
Candida Natural Language Processing Central Venous Catheters Italy Candidemia Female Aged Algorithms Male Critical Illness Humans Middle Aged Intensive Care Units Machine Learning Electronic Health Records
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Interpretable machine learning model for predicting acute kidney injury in critically ill patients.

BMC medical informatics and decision making May 31, 2024
BACKGROUND: This study aimed to create a method for promptly predicting acute kidney injury (AKI) in intensive care patients by applying interpretable, explainable artificial intelligence techniques.
Male Intensive Care Units Humans Middle Aged Female Machine Learning Aged Acute Kidney Injury Adult Critical Illness
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Machine learning derived serum creatinine trajectories in acute kidney injury in critically ill patients with sepsis.

Critical care (London, England) May 10, 2024
BACKGROUND: Current classification for acute kidney injury (AKI) in critically ill patients with sepsis relies only on its severity-measured by maximum creatinine which overlooks inherent complexities and longitudinal evaluation of this heterogenous ...
Middle Aged Sepsis Hospital Mortality Creatinine Intensive Care Units Biomarkers Humans Female Retrospective Studies Machine Learning Aged Acute Kidney Injury Male Critical Illness
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Association between the stress hyperglycemia ratio and 28-day all-cause mortality in critically ill patients with sepsis: a retrospective cohort study and predictive model establishment based on machine learning.

Cardiovascular diabetology May 9, 2024
BACKGROUND: Sepsis is a severe form of systemic inflammatory response syndrome that is caused by infection. Sepsis is characterized by a marked state of stress, which manifests as nonspecific physiological and metabolic changes in response to the dis...
Blood Glucose Time Factors Hospital Mortality Hyperglycemia Predictive Value of Tests Cause of Death Middle Aged Risk Assessment China Sepsis Female Biomarkers Humans Risk Factors Prognosis Machine Learning Aged Retrospective Studies Decision Support Techniques Male Databases, Factual Critical Illness
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