AIMC Topic: Critical Illness
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Machine learning prediction models for prognosis of critically ill patients after open-heart surgery.
Scientific reports
Feb 9, 2021
We aimed to build up multiple machine learning models to predict 30-days mortality, and 3 complications including septic shock, thrombocytopenia, and liver dysfunction after open-heart surgery. Patients who underwent coronary artery bypass surgery, a...
Addendum: Early triage of critically ill COVID-19 patients using deep learning.
Nature communications
Feb 1, 2021
Machine learning methods to improve bedside fluid responsiveness prediction in severe sepsis or septic shock: an observational study.
British journal of anaesthesia
Jan 16, 2021
BACKGROUND: Passive leg raising (PLR) predicts fluid responsiveness in critical illness, although restrictions in mobilising patients often preclude this haemodynamic challenge being used. We investigated whether machine learning applied on transthor...
Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes.
BMC medical informatics and decision making
Dec 30, 2020
BACKGROUND: Diabetes mellitus is a prevalent metabolic disease characterized by chronic hyperglycemia. The avalanche of healthcare data is accelerating precision and personalized medicine. Artificial intelligence and algorithm-based approaches are be...
Five novel clinical phenotypes for critically ill patients with mechanical ventilation in intensive care units: a retrospective and multi database study.
Respiratory research
Dec 10, 2020
BACKGROUND: Although protective mechanical ventilation (MV) has been used in a variety of applications, lung injury may occur in both patients with and without acute respiratory distress syndrome (ARDS). The purpose of this study is to use machine le...
Intensive Care Units
Female
Reproducibility of Results
Risk Factors
Aged
Phenotype
Databases, Factual
Retrospective Studies
Critical Illness
Respiration, Artificial
Lung
Treatment Outcome
Health Status
Predictive Value of Tests
Adult
Male
Middle Aged
Risk Assessment
Humans
Machine Learning
Decision Support Techniques
Ventilator-Induced Lung Injury
Do clinical and paraclinical findings have the power to predict critical conditions of injured patients after traumatic injury resuscitation? Using data mining artificial intelligence.
Chinese journal of traumatology = Zhonghua chuang shang za zhi
Nov 24, 2020
PURPOSE: The triage and initial care of injured patients and a subsequent right level of care is paramount for an overall outcome after traumatic injury. Early recognition of patients is an important case of such decision-making with risk of worse pr...
Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU.
BMC medical informatics and decision making
Oct 2, 2020
BACKGROUND: Early and accurate identification of sepsis patients with high risk of in-hospital death can help physicians in intensive care units (ICUs) make optimal clinical decisions. This study aimed to develop machine learning-based tools to predi...
Development of Machine Learning Models to Validate a Medication Regimen Complexity Scoring Tool for Critically Ill Patients.
The Annals of pharmacotherapy
Sep 15, 2020
INTRODUCTION: The Medication Regimen Complexity -Intensive Care Unit (MRC-ICU) is the first tool for measuring medication regimen complexity in critically ill patients. This study tested machine learning (ML) models to investigate the relationship be...
Utilization of machine-learning models to accurately predict the risk for critical COVID-19.
Internal and emergency medicine
Aug 18, 2020
Among patients with Coronavirus disease (COVID-19), the ability to identify patients at risk for deterioration during their hospital stay is essential for effective patient allocation and management. To predict patient risk for critical COVID-19 base...
Explainable artificial intelligence model to predict acute critical illness from electronic health records.
Nature communications
Jul 31, 2020
Acute critical illness is often preceded by deterioration of routinely measured clinical parameters, e.g., blood pressure and heart rate. Early clinical prediction is typically based on manually calculated screening metrics that simply weigh these pa...
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