Head Trauma

Discriminating stress from rest based on resting-state connectivity of the human brain: A supervised machine learning study.

Acute stress induces large-scale neural reorganization with relevance to stress-related psychopathology. Here, we applied a novel supervised machine learning method, combining the strengths of a priori theoretical insights with a data-driven approach, to identify which connectivity changes are most prominently associated with a state of acute stress and individual differences therein. Resting-stat...

Towards a fully automated surveillance of well-being status in laboratory mice using deep learning: Starting with facial expression analysis.

Assessing the well-being of an animal is hindered by the limitations of efficient communication between humans and animals. Instead of direct communication, a variety of parameters are employed to evaluate the well-being of an animal. Especially in the field of biomedical research, scientifically sound tools to assess pain, suffering, and distress for experimental animals are highly demanded due t...

Investigation of Machine Learning Approaches for Traumatic Brain Injury Classification via EEG Assessment in Mice.

Due to the difficulties and complications in the quantitative assessment of traumatic brain injury (TBI) and its increasing relevance in today's world...

Unsupervised Machine Learning Reveals Novel Traumatic Brain Injury Patient Phenotypes with Distinct Acute Injury Profiles and Long-Term Outcomes.

The heterogeneity of traumatic brain injury (TBI) remains a core challenge for the success of interventional clinical trials. Data-driven approaches f...

Detection of Moderate Traumatic Brain Injury from Resting-State Eye-Closed Electroencephalography.

Traumatic brain injury (TBI) is one of the injuries that can bring serious consequences if medical attention has been delayed. Commonly, analysis of c...

Latent Phase Identification of High-Frequency Micro-Scale Gamma Spike Transients in the Hypoxic Ischemic EEG of Preterm Fetal Sheep Using Spectral Analysis and Fuzzy Classifiers.

Premature babies are at high risk of serious neurodevelopmental disabilities, which in many cases are related to perinatal hypoxic-ischemic encephalop...

Convolutional neural network enhancement of fast-scan low-dose cone-beam CT images for head and neck radiotherapy.

To improve image quality and CT number accuracy of fast-scan low-dose cone-beam computed tomography (CBCT) through a deep-learning convolutional neura...

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...

Endocannabinoid degradation inhibitors ameliorate neuronal and synaptic alterations following traumatic brain injury.

Our previous work showed that lateral fluid percussion injury to the sensorimotor cortex (SMC) of anesthetized rats increased neuronal synaptic hypere...

Training and Validation of Deep Neural Networks for the Prediction of 90-Day Post-Liver Transplant Mortality Using UNOS Registry Data.

Prediction models of post-liver transplant mortality are crucial so that donor organs are not allocated to recipients with unreasonably high probabili...

Effects of post-weaning supplementation of immunomodulatory feed ingredient on body weight and cortisol concentrations in program-fed beef heifers.

The objective of this study was to determine the effects of an immunomodulatory feed ingredient during post-weaning on growth and cortisol concentrati...

Intracatheter Tissue Plasminogen Activator for Chronic Subdural Hematomas after Failed Bedside Twist Drill Craniostomy: A Retrospective Review.

Introduction Chronic subdural hematomas (cSDH) are common in neurosurgery with various symptoms and significant morbidity and mortality. Treatment var...

Machine learning for the detection of early immunological markers as predictors of multi-organ dysfunction.

The immune response to major trauma has been analysed mainly within post-hospital admission settings where the inflammatory response is already underw...

Machine learning-based dynamic mortality prediction after traumatic brain injury.

Our aim was to create simple and largely scalable machine learning-based algorithms that could predict mortality in a real-time fashion during intensi...

Development of artificial neural network model for prediction of post-streptococcus mutans in dental caries.

BACKGROUND AND OBJECTIVE: Streptococcus mutans is the primary initiator and most common organism associated with dental caries. Prediction of post-Str...

Comparative analysis on Facebook post interaction using DNN, ELM and LSTM.

This study presents a novel research approach to predict user interaction for social media post using machine learning algorithms. The posts are conve...

Association of polymorphisms in , , and with post-induction infliximab trough level in Crohn's disease patients.

BACKGROUND: Trough levels of the post-induction serum infliximab (IFX) are associated with short-term and long-term responses of Crohn's disease patie...

Choosing Clinical Variables for Risk Stratification Post-Acute Coronary Syndrome.

Most risk stratification methods use expert opinion to identify a fixed number of clinical variables that have prognostic significance. In this study ...

Predicting post-stroke pneumonia using deep neural network approaches.

BACKGROUND AND PURPOSE: Pneumonia is a common complication after stroke, causing an increased length of hospital stay and death. Therefore, the timely...

Machine Learning to Predict In-Hospital Morbidity and Mortality after Traumatic Brain Injury.

Recently, successful predictions using machine learning (ML) algorithms have been reported in various fields. However, in traumatic brain injury (TBI)...