AIMC Topic: Intensive Care Units

Clear Filters Showing 681 to 690 of 747 articles

Interpretable Machine Learning Model for Early Prediction of Mortality in ICU Patients with Rhabdomyolysis.

Medicine and science in sports and exercise
PURPOSE: Rhabdomyolysis (RM) is a complex set of clinical syndromes that involves the rapid dissolution of skeletal muscles. Mortality from RM is approximately 10%. This study aimed to develop an interpretable and generalizable model for early mortal...

Multitask prediction of organ dysfunction in the intensive care unit using sequential subnetwork routing.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Multitask learning (MTL) using electronic health records allows concurrent prediction of multiple endpoints. MTL has shown promise in improving model performance and training efficiency; however, it often suffers from negative transfer - i...

Predicting the Need For Vasopressors in the Intensive Care Unit Using an Attention Based Deep Learning Model.

Shock (Augusta, Ga.)
BACKGROUND: Previous models on prediction of shock mostly focused on septic shock and often required laboratory results in their models. The purpose of this study was to use deep learning approaches to predict vasopressor requirement for critically i...

Mortality risk stratification using artificial intelligence-augmented electrocardiogram in cardiac intensive care unit patients.

European heart journal. Acute cardiovascular care
AIMS: An artificial intelligence-augmented electrocardiogram (AI-ECG) algorithm can identify left ventricular systolic dysfunction (LVSD). We sought to determine whether this AI-ECG algorithm could stratify mortality risk in cardiac intensive care un...

Predicting ventilator-associated pneumonia with machine learning.

Medicine
Ventilator-associated pneumonia (VAP) is the most common and fatal nosocomial infection in intensive care units (ICUs). Existing methods for identifying VAP display low accuracy, and their use may delay antimicrobial therapy. VAP diagnostics derived ...

Predicting in-hospital mortality in ICU patients with sepsis using gradient boosting decision tree.

Medicine
Sepsis is a leading cause of mortality in the intensive care unit. Early prediction of sepsis can reduce the overall mortality rate and cost of sepsis treatment. Some studies have predicted mortality and development of sepsis using machine learning m...

Avoidable Serum Potassium Testing in the Cardiac ICU: Development and Testing of a Machine-Learning Model.

Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies
OBJECTIVES: To create a machine-learning model identifying potentially avoidable blood draws for serum potassium among pediatric patients following cardiac surgery.

Natural language processing to measure the frequency and mode of communication between healthcare professionals and family members of critically ill patients.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To apply natural language processing (NLP) techniques to identify individual events and modes of communication between healthcare professionals and families of critically ill patients from electronic medical records (EMR).

A Machine Learning Approach to Estimate the Glomerular Filtration Rate in Intensive Care Unit Patients Based on Plasma Iohexol Concentrations and Covariates.

Clinical pharmacokinetics
OBJECTIVE: This work aims to evaluate whether a machine learning approach is appropriate to estimate the glomerular filtration rate in intensive care unit patients based on sparse iohexol pharmacokinetic data and a limited number of predictors.

Drivers of Prolonged Hospitalization Following Spine Surgery: A Game-Theory-Based Approach to Explaining Machine Learning Models.

The Journal of bone and joint surgery. American volume
BACKGROUND: Understanding the interactions between variables that predict prolonged hospital length of stay (LOS) following spine surgery can help uncover drivers of this risk in patients. This study utilized a novel game-theory-based approach to dev...