AIMC Topic: Critical Illness

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Fast and interpretable mortality risk scores for critical care patients.

Journal of the American Medical Informatics Association : JAMIA Apr 1, 2025
OBJECTIVE: Prediction of mortality in intensive care unit (ICU) patients typically relies on black box models (that are unacceptable for use in hospitals) or hand-tuned interpretable models (that might lead to the loss in performance). We aim to brid...
Humans Machine Learning Risk Assessment Hospital Mortality Critical Care Algorithms Critical Illness Intensive Care Units
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[Establishing of mortality predictive model for elderly critically ill patients using simple bedside indicators and interpretable machine learning algorithms].

Zhonghua wei zhong bing ji jiu yi xue Feb 1, 2025
OBJECTIVE: To explore the feasibility of incorporating simple bedside indicators into death predictive model for elderly critically ill patients based on interpretability machine learning algorithms, providing a new scheme for clinical disease assess...
Organ Dysfunction Scores Male Prognosis Hospital Mortality Retrospective Studies APACHE Critical Illness Intensive Care Units Female Aged Humans Machine Learning Algorithms
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[Applications, challenges and future prospects of artificial intelligence in critical and acute cardiovascular care].

Zhonghua xin xue guan bing za zhi Nov 24, 2024
Critical Care Humans Artificial Intelligence Prognosis Critical Illness Cardiovascular Diseases Big Data
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[Deep Learning Reconstruction Algorithm Combined With Smart Metal Artifact Reduction Technique Improves Image Quality of Upper Abdominal CT in Critically Ill Patients].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition Nov 20, 2024
OBJECTIVE: To evaluate the effect of deep learning reconstruction algorithm combined with smart metal artifact reduction (DLMAR) on the quality of abdominal CT images in critically ill patients who are unable to raise their arms and require electroca...
Artifacts Signal-To-Noise Ratio Radiography, Abdominal Deep Learning Metals Image Processing, Computer-Assisted Critical Illness Female Aged Adult Male Middle Aged Algorithms Retrospective Studies Tomography, X-Ray Computed Humans
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Prediction of teicoplanin plasma concentration in critically ill patients: a combination of machine learning and population pharmacokinetics.

The Journal of antimicrobial chemotherapy Nov 4, 2024
BACKGROUND: Teicoplanin has been widely used in patients with infections caused by Staphylococcus aureus, especially for critically ill patients. The pharmacokinetics (PK) of teicoplanin vary between individuals and within the same individual. We aim...
Algorithms Anti-Bacterial Agents Retrospective Studies Humans Machine Learning Aged, 80 and over Adult Male Critical Illness Female Aged Middle Aged Staphylococcal Infections Teicoplanin
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Explainable artificial intelligence model for mortality risk prediction in the intensive care unit: a derivation and validation study.

Postgraduate medical journal Mar 18, 2024
BACKGROUND: The lack of transparency is a prevalent issue among the current machine-learning (ML) algorithms utilized for predicting mortality risk. Herein, we aimed to improve transparency by utilizing the latest ML explicable technology, SHapley Ad...
Algorithms Critical Illness Intensive Care Units Female Aged Male Humans Artificial Intelligence Middle Aged Critical Care
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Interpretable Machine Learning Approach for Predicting 30-Day Mortality of Critical Ill Patients with Pulmonary Embolism and Heart Failure: A Retrospective Study.

Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis Jan 1, 2024
BACKGROUND: Pulmonary embolism (PE) patients combined with heart failure (HF) have been reported to have a high short-term mortality. However, few studies have developed predictive tools of 30-day mortality for these patients in intensive care unit (...
Heart Failure Male Middle Aged Pulmonary Embolism Critical Illness Humans Female Machine Learning Aged Retrospective Studies
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Individualized Treatment Effects: Machine Learning Can Revolutionize Observations, but Let's Understand What We Are Observing.

American journal of respiratory and critical care medicine Jun 15, 2023
Critical Illness Calibration Intubation, Intratracheal Humans Machine Learning
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Continuous Renal Replacement Therapy: What Have We Learned And What Are Key Milestones For The Years To Come?

Revista de investigacion clinica; organo del Hospital de Enfermedades de la Nutricion Jan 1, 2023
Continuous renal replacement therapy (CRRT) is the main extracorporeal kidney support therapy used in critical ill patients in the intensive care unit (ICU). Since its conceptualization ~50 years ago, there have been major improvements in its technol...
Intensive Care Units Renal Replacement Therapy Humans Acute Kidney Injury Pandemics Critical Illness Continuous Renal Replacement Therapy
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Advances in artificial intelligence and deep learning systems in ICU-related acute kidney injury.

Current opinion in critical care Dec 1, 2021
PURPOSE OF REVIEW: Acute kidney injury (AKI) affects nearly 60% of all patients admitted to ICUs. Large volumes of clinical, monitoring and laboratory data produced in ICUs allow the application of artificial intelligence analytics. The purpose of th...
Humans Deep Learning Acute Kidney Injury Artificial Intelligence Critical Illness Intensive Care Units
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