AIMC Topic: Intensive Care Units
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Mortality risk stratification using artificial intelligence-augmented electrocardiogram in cardiac intensive care unit patients.
European heart journal. Acute cardiovascular care
Jun 30, 2021
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
Jun 11, 2021
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
May 14, 2021
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
Apr 1, 2021
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
Mar 1, 2021
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
Feb 1, 2021
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
Jan 6, 2021
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...
Cost-Effective Machine Learning Based Clinical Pre-Test Probability Strategy for DVT Diagnosis in Neurological Intensive Care Unit.
Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
Jan 1, 2021
In order to overcome the shortage of the current costly DVT diagnosis and reduce the waste of valuable healthcare resources, we proposed a new diagnostic approach based on machine learning pre-test prediction models using EHRs. We examined the sociod...
Artificial intelligence-based prediction of transfusion in the intensive care unit in patients with gastrointestinal bleeding.
BMJ health & care informatics
Jan 1, 2021
OBJECTIVE: Gastrointestinal (GI) bleeding commonly requires intensive care unit (ICU) in cases of potentialhaemodynamiccompromise or likely urgent intervention. However, manypatientsadmitted to the ICU stop bleeding and do not require further interve...
Democratizing EHR analyses with FIDDLE: a flexible data-driven preprocessing pipeline for structured clinical data.
Journal of the American Medical Informatics Association : JAMIA
Dec 9, 2020
OBJECTIVE: In applying machine learning (ML) to electronic health record (EHR) data, many decisions must be made before any ML is applied; such preprocessing requires substantial effort and can be labor-intensive. As the role of ML in health care gro...
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