AIMC Topic: Vital Signs
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[The future patient monitoring in the bed ward].
Ugeskrift for laeger
Aug 29, 2022
Current monitoring of vital signs in hospital wards rely on infrequent manual measurements. This narrative review describes how new wearable devices with artificial intelligence interpretation may overcome this challenge by providing nurses with cont...
Validation of a Machine Learning Model for Early Shock Detection.
Military medicine
Jan 4, 2022
OBJECTIVES: The objectives of this study were to test in real time a Trauma Triage, Treatment, and Training Decision Support (4TDS) machine learning (ML) model of shock detection in a prospective silent trial, and to evaluate specificity, sensitivity...
A New Time-Window Prediction Model For Traumatic Hemorrhagic Shock Based on Interpretable Machine Learning.
Shock (Augusta, Ga.)
Jan 1, 2022
Early warning prediction of traumatic hemorrhagic shock (THS) can greatly reduce patient mortality and morbidity. We aimed to develop and validate models with different stepped feature sets to predict THS in advance. From the PLA General Hospital Eme...
Predicting the Need For Vasopressors in the Intensive Care Unit Using an Attention Based Deep Learning Model.
Shock (Augusta, Ga.)
Jul 1, 2021
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...
A Time-Phased Machine Learning Model for Real-Time Prediction of Sepsis in Critical Care.
Critical care medicine
Oct 1, 2020
OBJECTIVES: As a life-threatening condition, sepsis is one of the major public health issues worldwide. Early prediction can improve sepsis outcomes with appropriate interventions. With the PhysioNet/Computing in Cardiology Challenge 2019, we aimed t...
Machine Learning Classifier Models Can Identify Acute Respiratory Distress Syndrome Phenotypes Using Readily Available Clinical Data.
American journal of respiratory and critical care medicine
Oct 1, 2020
Two distinct phenotypes of acute respiratory distress syndrome (ARDS) with differential clinical outcomes and responses to randomly assigned treatment have consistently been identified in randomized controlled trial cohorts using latent class analys...
Serum Albumin
Mortality
Bicarbonates
Leukocyte Count
Platelet Count
Bilirubin
Pulmonary Ventilation
Vital Signs
Receptors, Tumor Necrosis Factor, Type I
Partial Pressure
Protein C
Area Under Curve
Interleukin-8
Oxygen
Intercellular Adhesion Molecule-1
Creatinine
Vasoconstrictor Agents
Carbon Dioxide
Plasminogen Activator Inhibitor 1
Respiratory Distress Syndrome
Randomized Controlled Trials as Topic
Tidal Volume
Blood Pressure
Biomarkers, Tumor
Latent Class Analysis
Phenotype
Inflammation
Interleukin-6
Age Factors
Prognosis
Humans
Machine Learning
Adding Continuous Vital Sign Information to Static Clinical Data Improves the Prediction of Length of Stay After Intubation: A Data-Driven Machine Learning Approach.
Respiratory care
Sep 1, 2020
BACKGROUND: Bedside monitors in the ICU routinely measure and collect patients' physiologic data in real time to continuously assess the health status of patients who are critically ill. With the advent of increased computational power and the abilit...
Combat medic testing of a novel monitoring capability for early detection of hemorrhage.
The journal of trauma and acute care surgery
Aug 1, 2020
BACKGROUND: Current out-of-hospital protocols to determine hemorrhagic shock in civilian trauma systems rely on standard vital signs with military guidelines relying on heart rate and strength of the radial pulse on palpation, all of which have prove...
Validating clinical threshold values for a dashboard view of the compensatory reserve measurement for hemorrhage detection.
The journal of trauma and acute care surgery
Aug 1, 2020
BACKGROUND: Compensatory reserve measurement (CRM) is a novel noninvasive monitoring technology designed to assess physiologic reserve using feature interrogation of arterial pulse waveforms. This study was conducted to validate clinically relevant C...
Detecting Patient Deterioration Using Artificial Intelligence in a Rapid Response System.
Critical care medicine
Apr 1, 2020
OBJECTIVES: As the performance of a conventional track and trigger system in a rapid response system has been unsatisfactory, we developed and implemented an artificial intelligence for predicting in-hospital cardiac arrest, denoted the deep learning...
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