Bioterrorism

Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone-based survey when cities and towns are under quarantine.

We propose the use of a machine learning algorithm to improve possible COVID-19 case identification more quickly using a mobile phone-based web survey. This method could reduce the spread of the virus in susceptible populations under quarantine.

Machine learning for syndromic surveillance using veterinary necropsy reports.

The use of natural language data for animal population surveillance represents a valuable opportunity to gather information about potential disease outbreaks, emerging zoonotic diseases, or bioterrorism threats. In this study, we evaluate machine learning methods for conducting syndromic surveillance using free-text veterinary necropsy reports. We train a system to detect if a necropsy report from...

Somatosensory evoked fields predict response to vagus nerve stimulation.

There is an unmet need to develop robust predictive algorithms to preoperatively identify pediatric epilepsy patients who will respond to vagus nerve ...

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...

Propagation of uncertainty in the mechanical and biological response of growing tissues using multi-fidelity Gaussian process regression.

A key feature of living tissues is their capacity to remodel and grow in response to environmental cues. Within continuum mechanics, this process can ...

Radical systems thinking and the future role of computational modelling in ergonomics: an exploration of agent-based modelling.

We are teetering on the precipice of the imminent Fourth Industrial Revolution. In this new age, systems are set to become more densely intraconnected...

Machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy.

In patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy (NAC), some patients achieve a complete pathologic response (pCR),...

ARPNet: Antidepressant Response Prediction Network for Major Depressive Disorder.

Treating patients with major depressive disorder is challenging because it takes several months for antidepressants prescribed for the patients to tak...

A comparison of machine learning algorithms for the surveillance of autism spectrum disorder.

OBJECTIVE: The Centers for Disease Control and Prevention (CDC) coordinates a labor-intensive process to measure the prevalence of autism spectrum dis...

Machine learning and data mining frameworks for predicting drug response in cancer: An overview and a novel in silico screening process based on association rule mining.

A major challenge in cancer treatment is predicting the clinical response to anti-cancer drugs on a personalized basis. The success of such a task lar...

Envisioning the expertise of the future.

Envisioning the expertise of the future in the field of food safety is challenging, as society, science and the way we work and live are changing and ...

mSphere of Influence: the Rise of Artificial Intelligence in Infection Biology.

Artur Yakimovich works in the field of computational virology and applies machine learning algorithms to study host-pathogen interactions. In this mSp...

Ventricular geometry-regularized QRSd predicts cardiac resynchronization therapy response: machine learning from crosstalk between electrocardiography and echocardiography.

Up to one-third of patients selected by current guidelines do not respond to cardiac resynchronization therapy (CRT), the aim of this study was to fin...

Regional level influenza study based on Twitter and machine learning method.

The significance of flu prediction is that the appropriate preventive and control measures can be taken by relevant departments after assessing predic...

Characterisation of nonlinear receptive fields of visual neurons by convolutional neural network.

A comprehensive understanding of the stimulus-response properties of individual neurons is necessary to crack the neural code of sensory cortices. How...

A Parametric Design Method for Optimal Quick Diagnostic Software.

Fault diagnostic software is required to respond to faults as early as possible in time-critical applications. However, the existing methods based on ...

Predicting drug response of tumors from integrated genomic profiles by deep neural networks.

BACKGROUND: The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic fea...

Quantitative Electroencephalography in Guiding Treatment of Major Depression.

This paper reviews significant contributions to the evidence for the use of quantitative electroencephalography features as biomarkers of depression t...

Objective auditory brainstem response classification using machine learning.

OBJECTIVE: The objective of this study was to use machine learning in the form of a deep neural network to objectively classify paired auditory brains...

Leveraging Machine Learning Approaches for Predicting Antidepressant Treatment Response Using Electroencephalography (EEG) and Clinical Data.

Individuals with major depressive disorder (MDD) vary in their response to antidepressants. However, identifying objective biomarkers, prior to or ea...