Emergency Medicine

Prehospital triage of acute aortic syndrome using a machine learning algorithm.

BACKGROUND: Acute aortic syndrome (AAS) comprises a complex and potentially fatal group of conditions requiring emergency specialist management. The aim of this study was to build a prediction algorithm to assist prehospital triage of AAS.

Exploring linearity of deep neural network trained QSM: QSMnet.

Recently, deep neural network-powered quantitative susceptibility mapping (QSM), QSMnet, successfully performed ill-conditioned dipole inversion in QSM and generated high-quality susceptibility maps. In this paper, the network, which was trained by healthy volunteer data, is evaluated for hemorrhagic lesions that have substantially higher susceptibility than healthy tissues in order to test "linea...

Image Based Liver Toxicity Prediction.

The drugs we use to cure our diseases can cause damage to the liver as it is the primary organ responsible for metabolism of environmental chemicals a...

A Novel 10-Point Score System to Predict Early Hematoma Growth in Patients With Spontaneous Intracerebral Hemorrhage.

A rapid and reliable method to predict significant early hematoma growth in the acute setting is of great important to better inform clinicians and r...

A machine learning approach for handling big data produced by high resolution mass spectrometry after data independent acquisition of small molecules - Proof of concept study using an artificial neural network for sample classification.

Liquid chromatography coupled to high-resolution mass spectrometry (HRMS) enables data independent acquisition (DIA) and untargeted screening. However...

Mixed-integer optimization approach to learning association rules for unplanned ICU transfer.

After admission to emergency department (ED), patients with critical illnesses are transferred to intensive care unit (ICU) due to unexpected clinical...

Development and Evaluation of a Deep Learning System for Screening Retinal Hemorrhage Based on Ultra-Widefield Fundus Images.

PURPOSE: To develop and evaluate a deep learning (DL) system for retinal hemorrhage (RH) screening using ultra-widefield fundus (UWF) images.

Prediction of Early TBI Mortality Using a Machine Learning Approach in a LMIC Population.

In a time when the incidence of severe traumatic brain injury (TBI) is increasing in low- to middle-income countries (LMICs), it is important to unde...

A six‑gene support vector machine classifier contributes to the diagnosis of pediatric septic shock.

Septic shock is induced by an uncontrolled inflammatory immune response to pathogens and the survival rate of patients with pediatric septic shock (PS...

Investigating the use of data-driven artificial intelligence in computerised decision support systems for health and social care: A systematic review.

There is growing interest in the potential of artificial intelligence to support decision-making in health and social care settings. There is, however...

Ovarian torsion: developing a machine-learned algorithm for diagnosis.

BACKGROUND: Ovarian torsion is a common concern in girls presenting to emergency care with pelvic or abdominal pain. The diagnosis is challenging to m...

Machine learning models for identifying preterm infants at risk of cerebral hemorrhage.

Intracerebral hemorrhage in preterm infants is a major cause of brain damage and cerebral palsy. The pathogenesis of cerebral hemorrhage is multifacto...

Early Recognition of Burn- and Trauma-Related Acute Kidney Injury: A Pilot Comparison of Machine Learning Techniques.

Severely burned and non-burned trauma patients are at risk for acute kidney injury (AKI). The study objective was to assess the theoretical performanc...

Deep learning, computer-aided radiography reading for tuberculosis: a diagnostic accuracy study from a tertiary hospital in India.

In general, chest radiographs (CXR) have high sensitivity and moderate specificity for active pulmonary tuberculosis (PTB) screening when interpreted ...

Deep learning in fracture detection: a narrative review.

Artificial intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI, p...

Systematic Modeling of log  Based on Ensemble Machine Learning, Group Contribution, and Matched Molecular Pair Analysis.

Lipophilicity, as evaluated by the -octanol/buffer solution distribution coefficient at pH = 7.4 (log ), is a major determinant of various absorption,...

Multi-Modal Diagnosis of Infectious Diseases in the Developing World.

In low and middle income countries, infectious diseases continue to have a significant impact, particularly amongst the poorest in society. Tetanus an...

Rapid Assessment of Opioid Exposure and Treatment in Cities Through Robotic Collection and Chemical Analysis of Wastewater.

INTRODUCTION: Accurate data regarding opioid use, overdose, and treatment is important in guiding community efforts at combating the opioid epidemic. ...

Automated screening of natural language in electronic health records for the diagnosis septic shock is feasible and outperforms an approach based on explicit administrative codes.

PURPOSE: Identification of patients for epidemiologic research through administrative coding has important limitations. We investigated the feasibilit...

System Design for Emergency Alert Triggered by Falls Using Convolutional Neural Networks.

The world population ageing is on the rise, which has led to an increase in the demand for medical care due to diseases and symptoms prevalent in heal...