AIMC Topic: Intensive Care Units

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Machine Learning-Based Prediction Model for ICU Mortality After Continuous Renal Replacement Therapy Initiation in Children.

Critical care explorations Dec 17, 2024
BACKGROUND: Continuous renal replacement therapy (CRRT) is the favored renal replacement therapy in critically ill patients. Predicting clinical outcomes for CRRT patients is difficult due to population heterogeneity, varying clinical practices, and ...
Child, Preschool Child Hospital Mortality ROC Curve Retrospective Studies Female Male Continuous Renal Replacement Therapy Infant Acute Kidney Injury Adolescent Critical Illness Intensive Care Units Humans Machine Learning
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Machine Learning for In-hospital Mortality Prediction in Critically Ill Patients With Acute Heart Failure: A Retrospective Analysis Based on the MIMIC-IV Database.

Journal of cardiothoracic and vascular anesthesia Dec 16, 2024
BACKGROUND: The incidence, mortality, and readmission rates for acute heart failure (AHF) are high, and the in-hospital mortality for AHF patients in the intensive care unit (ICU) is higher. However, there is currently no method to accurately predict...
Aged, 80 and over Male Middle Aged Hospital Mortality Predictive Value of Tests Humans Critical Illness Intensive Care Units Machine Learning Female Databases, Factual Aged Retrospective Studies Acute Disease Heart Failure
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Utility of an Echocardiographic Machine Learning Model to Predict Outcomes in Intensive Cardiac Care Unit Patients.

Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography Dec 14, 2024
INTRODUCTION: The risk stratification at admission to the intensive cardiac care unit (ICCU) is crucial and remains challenging.
Humans Machine Learning Prospective Studies Prognosis Intensive Care Units Female Aged Male Echocardiography Middle Aged Risk Assessment Hospital Mortality Coronary Care Units
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A Machine Learning Approach for Predicting In-Hospital Cardiac Arrest Using Single-Day Vital Signs, Laboratory Test Results, and International Classification of Disease-10 Block for Diagnosis.

Annals of laboratory medicine Dec 13, 2024
BACKGROUND: Predicting in-hospital cardiac arrest (IHCA) is crucial for potentially reducing mortality and improving patient outcomes. However, most models, which rely solely on vital signs, may not comprehensively capture the patients' risk profiles...
Intensive Care Units Female Hospitalization Aged Aged, 80 and over Humans Machine Learning Retrospective Studies International Classification of Diseases Vital Signs Heart Arrest Adult Male Middle Aged ROC Curve
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Employing artificial intelligence for optimising antibiotic dosages in sepsis on intensive care unit: a study protocol for a prospective observational study (KI.SEP).

BMJ open Dec 12, 2024
INTRODUCTION: In sepsis treatment, achieving and maintaining effective antibiotic therapy is crucial. However, optimal antibiotic dosing faces challenges due to significant variability among patients with sepsis. Therapeutic drug monitoring (TDM), th...
Piperacillin, Tazobactam Drug Combination Anti-Bacterial Agents Prospective Studies Intensive Care Units Female Sepsis Observational Studies as Topic Drug Monitoring Meropenem Germany Humans Artificial Intelligence Machine Learning Research Design
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Utilizing deep learning-based causal inference to explore vancomycin's impact on continuous kidney replacement therapy necessity in blood culture-positive intensive care unit patients.

Microbiology spectrum Dec 10, 2024
Patients with positive blood cultures in the intensive care unit (ICU) are at high risk for septic acute kidney injury requiring continuous kidney replacement therapy (CKRT), especially when treated with vancomycin. This study developed a machine lea...
Deep Learning Sepsis Retrospective Studies Blood Culture Continuous Renal Replacement Therapy Vancomycin Acute Kidney Injury Intensive Care Units Female Humans Aged Machine Learning Adult Anti-Bacterial Agents Male Middle Aged
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Development and application of an intelligent pressure injury assessment system using AI image recognition.

Technology and health care : official journal of the European Society for Engineering and Medicine Dec 9, 2024
BackgroundPressure injuries are a significant concern in clinical settings, requiring accurate assessment to prevent complications. Traditional assessment methods are often subjective and time-consuming.ObjectiveThis study aimed to develop and evalua...
Adult Intensive Care Units Male Pressure Ulcer Humans Electronic Health Records Middle Aged Neural Networks, Computer Female Deep Learning Artificial Intelligence Aged Algorithms
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A novel approach to antimicrobial resistance: Machine learning predictions for carbapenem-resistant Klebsiella in intensive care units.

International journal of medical informatics Dec 7, 2024
This study was conducted at Kocaeli University Hospital in Turkey and aimed to predict carbapenem-resistant Klebsiella pneumoniae infection in intensive care units using the Extreme Gradient Boosting (XGBoost) algorithm, a form of artificial intellig...
Klebsiella Infections Turkey Carbapenems Carbapenem-Resistant Enterobacteriaceae Retrospective Studies Intensive Care Units Female Adult Aged Humans Male Machine Learning Middle Aged Algorithms Case-Control Studies Anti-Bacterial Agents Klebsiella pneumoniae Drug Resistance, Bacterial
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Multi-horizon event detection for in-hospital clinical deterioration using dual-channel graph attention network.

International journal of medical informatics Dec 6, 2024
OBJECTIVE: In hospitals globally, the occurrence of clinical deterioration within the hospital setting poses a significant healthcare burden. Rapid clinical intervention becomes a crucial task in such cases. In this research, we propose an end-to-end...
Humans Neural Networks, Computer Clinical Deterioration Algorithms Intensive Care Units Deep Learning ROC Curve
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Early prediction of intensive care unit admission in emergency department patients using machine learning.

Australian critical care : official journal of the Confederation of Australian Critical Care Nurses Dec 5, 2024
BACKGROUND: The timely identification and transfer of critically ill patients from the emergency department (ED) to the intensive care unit (ICU) is important for patient care and ED workflow practices.
Humans Machine Learning Retrospective Studies Critical Illness Intensive Care Units Male Middle Aged Triage Electronic Health Records Emergency Service, Hospital Female Patient Admission Aged Adult
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