AIMC Topic: Glasgow Coma Scale
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Anomaly Detection of Moderate Traumatic Brain Injury Using Auto-Regularized Multi-Instance One-Class SVM.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Oct 24, 2019
Detection and quantification of functional deficits due to moderate traumatic brain injury (mTBI) is crucial for clinical decision-making and timely commencement of functional therapy. In this work, we explore magnetoencephalography (MEG) based funct...
Prediction of hematoma expansion in spontaneous intracerebral hemorrhage using support vector machine.
EBioMedicine
May 3, 2019
BACKGROUND: Spontaneous intracerebral hemorrhage (ICH) is a devastating disease with high mortality rate. This study aimed to predict hematoma expansion in spontaneous ICH from routinely available variables by using support vector machine (SVM) metho...
Support Vector Machine
Cerebral Hemorrhage
Models, Biological
Hematoma
Odds Ratio
Glasgow Coma Scale
Sensitivity and Specificity
Humans
Male
Middle Aged
Biomarkers
ROC Curve
Prognosis
Aged
Image Processing, Computer-Assisted
Retrospective Studies
Aged, 80 and over
Tomography, X-Ray Computed
Young Adult
Female
Adult
Prediction of cervical spine injury in young pediatric patients: an optimal trees artificial intelligence approach.
Journal of pediatric surgery
Mar 18, 2019
BACKGROUND: Cervical spine injuries (CSI) are a major concern in young pediatric trauma patients. The consequences of missed injuries and difficulties in injury clearance for non-verbal patients have led to a tendency to image young children. Imaging...
Impact of guidelines for the management of minor head injury on the utilization and diagnostic yield of CT over two decades, using natural language processing in a large dataset.
European radiology
Jan 14, 2019
OBJECTIVES: We investigated the impact of clinical guidelines for the management of minor head injury on utilization and diagnostic yield of head CT over two decades.
Comparison of machine learning models for the prediction of mortality of patients with unplanned extubation in intensive care units.
Scientific reports
Nov 20, 2018
Unplanned extubation (UE) can be associated with fatal outcome; however, an accurate model for predicting the mortality of UE patients in intensive care units (ICU) is lacking. Therefore, we aim to compare the performances of various machine learning...
A data-driven artificial intelligence model for remote triage in the prehospital environment.
PloS one
Oct 23, 2018
In a mass casualty incident, the factors that determine the survival rate of injured patients are diverse, but one of the key factors is the time for triage. Additionally, the main factor that determines the time of triage is the number of medical pe...
Reliability of Robotic Telemedicine for Assessing Critically Ill Patients with the Full Outline of UnResponsiveness Score and Glasgow Coma Scale.
Telemedicine journal and e-health : the official journal of the American Telemedicine Association
Jan 13, 2017
PURPOSE: Telemedicine is increasingly utilized in the evaluation of critically ill patients, including those with decreased level of consciousness (LOC) or coma. Improving access to providers with neurologic expertise affords earlier triage and direc...
Machine learning-based prediction of clinical outcomes after traumatic brain injury: Hidden information of early physiological time series.
CNS neuroscience & therapeutics
Jul 1, 2024
AIMS: To assess the predictive value of early-stage physiological time-series (PTS) data and non-interrogative electronic health record (EHR) signals, collected within 24 h of ICU admission, for traumatic brain injury (TBI) patient outcomes.
[Personalized glycemic management for patients with diabetic ketoacidosis based on machine learning].
Zhonghua wei zhong bing ji jiu yi xue
Jun 1, 2024
OBJECTIVE: To explore the optimal blood glucose-lowering strategies for patients with diabetic ketoacidosis (DKA) to enhance personalized treatment effects using machine learning techniques based on the United States Critical Care Medical Information...
Predicting outcomes after trauma: Prognostic model development based on admission features through machine learning.
Medicine
Dec 10, 2021
In an overcrowded emergency department (ED), trauma surgeons and emergency physicians need an accurate prognostic predictor for critical decision-making involving patients with severe trauma. We aimed to develope a machine learning-based early progno...
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