Emergency Medicine

The development an artificial intelligence algorithm for early sepsis diagnosis in the intensive care unit.

BACKGROUND: Severe sepsis and septic shock are still the leading causes of death in Intensive Care Units (ICUs), and timely diagnosis is crucial for treatment outcomes. The progression of electronic medical records (EMR) offers the possibility of storing a large quantity of clinical data that can facilitate the development of artificial intelligence (AI) in medicine. However, several difficulties,...

Fully Automated Segmentation of Globes for Volume Quantification in CT Images of Orbits using Deep Learning.

BACKGROUND AND PURPOSE: Fast and accurate quantification of globe volumes in the event of an ocular trauma can provide clinicians with valuable diagnostic information. In this work, an automated workflow using a deep learning-based convolutional neural network is proposed for prediction of globe contours and their subsequent volume quantification in CT images of the orbits.

Chinese Emergency Event Recognition Using Conv-RDBiGRU Model.

In view of the weak generalization of traditional event recognition methods, the limitation of dependence on field knowledge of expert, the longer tra...

CT window trainable neural network for improving intracranial hemorrhage detection by combining multiple settings.

Window settings to rescale and contrast stretch raw data from radiographic images such as Computed Tomography (CT), X-ray and Magnetic Resonance image...

Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms.

Deep neural networks (DNN) are state-of-the-art machine learning algorithms that can be learned to self-extract significant features of the electrocar...

Osteoporotic hip fracture prediction from risk factors available in administrative claims data - A machine learning approach.

OBJECTIVE: Hip fractures are among the most frequently occurring fragility fractures in older adults, associated with a loss of quality of life, high ...

Predicting hospital admission for older emergency department patients: Insights from machine learning.

BACKGROUND: Emergency departments (ED) are a portal of entry into the hospital and are uniquely positioned to influence the health care trajectories o...

Automatic Triage of 12-Lead ECGs Using Deep Convolutional Neural Networks.

BACKGROUND The correct interpretation of the ECG is pivotal for the accurate diagnosis of many cardiac abnormalities, and conventional computerized in...

Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets.

Under the global pandemic of COVID-19, the use of artificial intelligence to analyze chest X-ray (CXR) image for COVID-19 diagnosis and patient triage...

BPBSAM: Body part-specific burn severity assessment model.

BACKGROUND AND OBJECTIVE: Burns are a serious health problem leading to several thousand deaths annually, and despite the growth of science and techno...

Use of CT and artificial intelligence in suspected or COVID-19 positive patients: statement of the Italian Society of Medical and Interventional Radiology.

The COVID-19 pandemic started in Italy in February 2020 with an exponential growth that has exceeded the number of cases reported in China. Italian ra...

Construction of a Machine Learning Dataset through Collaboration: The RSNA 2019 Brain CT Hemorrhage Challenge.

This dataset is composed of annotations of the five hemorrhage subtypes (subarachnoid, intraventricular, subdural, epidural, and intraparenchymal hemo...

Multiple Group Decision Making for Selecting Emergency Alternatives: A Novel Method Based on the LDWPA Operator and LD-MABAC.

When an emergency event occurs, it is critical to respond in the shortest possible time. Therefore, the rationality and effectiveness of emergency dec...

Emergency department disposition prediction using a deep neural network with integrated clinical narratives and structured data.

BACKGROUND: Emergency department (ED) overcrowding has been a serious issue and demands effective clinical decision-making of patient disposition. In ...

Association Rule Learning Is an Easy and Efficient Method for Identifying Profiles of Traumas and Stressors that Predict Psychopathology in Disaster Survivors: The Example of Sri Lanka.

Research indicates that psychopathology in disaster survivors is a function of both experienced trauma and stressful life events. However, such studie...

Exploring the Role of Artificial Intelligence in an Emergency and Trauma Radiology Department.

Emergency and trauma radiologists, emergency department's physicians and nurses, researchers, departmental leaders, and health policymakers have attem...

On the boundary conditions of avoidance memory reconsolidation: An attractor network perspective.

The reconsolidation and extinction of aversive memories and their boundary conditions have been extensively studied. Knowing their network mechanisms ...

Revealing cytotoxic substructures in molecules using deep learning.

In drug development, late stage toxicity issues of a compound are the main cause of failure in clinical trials. In silico methods are therefore of hig...

An Integrated Approach of Machine Learning and Systems Thinking for Waiting Time Prediction in an Emergency Department.

OBJECTIVE: The objective of this study is to apply machine learning algorithms for real-time and personalized waiting time prediction in emergency dep...

Development of a Bioinformatics Framework for Identification and Validation of Genomic Biomarkers and Key Immunopathology Processes and Controllers in Infectious and Non-infectious Severe Inflammatory Response Syndrome.

Sepsis is defined as dysregulated host response caused by systemic infection, leading to organ failure. It is a life-threatening condition, often requ...