AIMC Topic: Intensive Care Units
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Machine Learning-Based Prediction Models of Mortality for Intensive Care Unit Patients Using Nursing Records.
Studies in health technology and informatics
Jul 24, 2024
This study aimed to develop ICU mortality prediction models using a conceptual framework, focusing on nurses' concerns reflected in nursing records from the MIMIC IV database. We included 46,693 first-time ICU admissions of adults over 18 years with ...
A Deep-Learning-Based Approach for Delirium Monitoring in ICU Patients Using Thermograms.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
Patients in the ICU frequently suffer from delirium, which can delay their recovery and may cause significant distress. Despite standardized scoring systems, its diagnosis and classification however, remain largely subjective and are subject to intra...
Machine learning-based prediction of clinical outcomes after traumatic brain injury: Hidden information of early physiological time series.
CNS neuroscience & therapeutics
Jul 1, 2024
AIMS: To assess the predictive value of early-stage physiological time-series (PTS) data and non-interrogative electronic health record (EHR) signals, collected within 24 h of ICU admission, for traumatic brain injury (TBI) patient outcomes.
Predicting the unpredictable: a novel application of artificial intelligence in the cardiac intensive care unit.
European heart journal. Acute cardiovascular care
Jun 30, 2024
Development and external validation of a dynamic risk score for early prediction of cardiogenic shock in cardiac intensive care units using machine learning.
European heart journal. Acute cardiovascular care
Jun 30, 2024
AIMS: Myocardial infarction and heart failure are major cardiovascular diseases that affect millions of people in the USA with morbidity and mortality being highest among patients who develop cardiogenic shock. Early recognition of cardiogenic shock ...
[Personalized glycemic management for patients with diabetic ketoacidosis based on machine learning].
Zhonghua wei zhong bing ji jiu yi xue
Jun 1, 2024
OBJECTIVE: To explore the optimal blood glucose-lowering strategies for patients with diabetic ketoacidosis (DKA) to enhance personalized treatment effects using machine learning techniques based on the United States Critical Care Medical Information...
Effectiveness of an Artificial Intelligence-Enabled Intervention for Detecting Clinical Deterioration.
JAMA internal medicine
May 1, 2024
IMPORTANCE: Inpatient clinical deterioration is associated with substantial morbidity and mortality but may be easily missed by clinicians. Early warning scores have been developed to alert clinicians to patients at high risk of clinical deterioratio...
[Analysis of clinical treatment of acute respiratory distress syndrome assisted by artificial intelligence].
Zhonghua wei zhong bing ji jiu yi xue
Apr 1, 2024
OBJECTIVE: To evaluate the clinical practice of intensive care unit (ICU) physicians at Hebei General Hospital in identifying patients meeting the diagnostic criteria for acute respiratory distress syndrome (ARDS) and the current status of invasive m...
[Constructing a predictive model for the death risk of patients with septic shock based on supervised machine learning algorithms].
Zhonghua wei zhong bing ji jiu yi xue
Apr 1, 2024
OBJECTIVE: To construct and validate the best predictive model for 28-day death risk in patients with septic shock based on different supervised machine learning algorithms.
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...
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