AIMC Topic: Critical Illness

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Risk prediction of clinical adverse outcomes with machine learning in a cohort of critically ill patients with atrial fibrillation.

Scientific reports Sep 23, 2021
Critically ill patients affected by atrial fibrillation are at high risk of adverse events: however, the actual risk stratification models for haemorrhagic and thrombotic events are not validated in a critical care setting. With this paper we aimed t...
Female Risk Factors Aged Aged, 80 and over Retrospective Studies Critical Illness Intensive Care Units Atrial Fibrillation Hemorrhage Treatment Failure Ischemic Attack, Transient Male Risk Assessment Hospital Mortality Stroke Humans Machine Learning
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Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort.

Critical care (London, England) Aug 17, 2021
BACKGROUND: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine le...
Humans Machine Learning Electronic Health Records Female Aged Adult Male Outcome Assessment, Health Care Middle Aged COVID-19 Germany Cohort Studies Critical Illness Emergency Service, Hospital Intensive Care Units
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Magnetic Resonance Imaging Features of Cerebral Infarction in Critical Patients Based on Convolutional Neural Network.

Contrast media & molecular imaging Jul 26, 2021
The clinical application of the artificial intelligence-assisted system in imaging was investigated by analyzing the magnetic resonance imaging (MRI) influence characteristics of cerebral infarction in critically ill patients based on the convolution...
Male Image Processing, Computer-Assisted Prognosis Humans Magnetic Resonance Imaging Follow-Up Studies Critical Illness Neural Networks, Computer Cerebral Infarction Artificial Intelligence Female Image Interpretation, Computer-Assisted Aged
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Predicting lethal courses in critically ill COVID-19 patients using a machine learning model trained on patients with non-COVID-19 viral pneumonia.

Scientific reports Jun 24, 2021
In a pandemic with a novel disease, disease-specific prognosis models are available only with a delay. To bridge the critical early phase, models built for similar diseases might be applied. To test the accuracy of such a knowledge transfer, we inves...
COVID-19 Prognosis Retrospective Studies Critical Illness Humans Machine Learning Middle Aged Models, Theoretical Female Risk Factors Aged Male
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The Physiological Deep Learner: First application of multitask deep learning to predict hypotension in critically ill patients.

Artificial intelligence in medicine May 29, 2021
Critical care clinicians are trained to analyze simultaneously multiple physiological parameters to predict critical conditions such as hemodynamic instability. We developed the Multi-task Learning Physiological Deep Learner (MTL-PDL), a deep learnin...
Critical Care Arterial Pressure Humans Critical Illness Deep Learning Hypotension
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COVID-19 and the kidney: A retrospective analysis of 37 critically ill patients using machine learning.

PloS one May 20, 2021
INTRODUCTION: There is evidence that SARS-CoV2 has a particular affinity for kidney tissue and is often associated with kidney failure.
Retrospective Studies Renal Replacement Therapy Critical Illness Female Machine Learning COVID-19 Humans Prognosis Middle Aged Aged Renal Insufficiency, Chronic Aged, 80 and over Body Mass Index Adult Proteinuria Male
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Prediction of weaning from mechanical ventilation using Convolutional Neural Networks.

Artificial intelligence in medicine May 5, 2021
Weaning from mechanical ventilation covers the process of liberating the patient from mechanical support and removing the associated endotracheal tube. The management of weaning from mechanical ventilation comprises a significant proportion of the ca...
Ventilator Weaning Humans Neural Networks, Computer Critical Illness Intensive Care Units Respiration, Artificial
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A deep-learning model to continuously predict severe acute kidney injury based on urine output changes in critically ill patients.

Journal of nephrology Apr 26, 2021
BACKGROUND: Acute Kidney Injury (AKI), a frequent complication of pateints in the Intensive Care Unit (ICU), is associated with a high mortality rate. Early prediction of AKI is essential in order to trigger the use of preventive care actions.
Critical Illness Intensive Care Units Deep Learning Humans Retrospective Studies Acute Kidney Injury
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Machine Learning-Based Prediction of COVID-19 Severity and Progression to Critical Illness Using CT Imaging and Clinical Data.

Korean journal of radiology Mar 9, 2021
OBJECTIVE: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables.
Male Middle Aged Severity of Illness Index ROC Curve Retrospective Studies Tomography, X-Ray Computed Critical Illness SARS-CoV-2 Humans Machine Learning COVID-19
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Semi-automated tracking of pain in critical care patients using artificial intelligence: a retrospective observational study.

Scientific reports Mar 4, 2021
Monitoring the pain intensity in critically ill patients is crucial because intense pain can cause adverse events, including poor survival rates; however, continuous pain evaluation is difficult. Vital signs have traditionally been considered ineffec...
Humans Artificial Intelligence Machine Learning Retrospective Studies Support Vector Machine Pain Measurement Pain Japan Critical Care Adult Male Middle Aged Logistic Models Critical Illness Female Aged
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