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Significant shared heritability underlies suicide attempt and clinically predicted probability of attempting suicide.

Molecular psychiatry
Suicide accounts for nearly 800,000 deaths per year worldwide with rates of both deaths and attempts rising. Family studies have estimated substantial heritability of suicidal behavior; however, collecting the sample sizes necessary for successful ge...

Machine learning in suicide science: Applications and ethics.

Behavioral sciences & the law
For decades, our ability to predict suicide has remained at near-chance levels. Machine learning has recently emerged as a promising tool for advancing suicide science, particularly in the domain of suicide prediction. The present review provides an ...

Mapping the Delirium Literature Through Probabilistic Topic Modeling and Network Analysis: A Computational Scoping Review.

Psychosomatics
BACKGROUND: Delirium is an acute confusional state, associated with morbidity and mortality in diverse medically-ill populations. Delirium is recognized, through both professional competencies and instructional materials, as a core topic in consultat...

Blind Channel and Data Estimation Using Fuzzy Logic-Empowered Opposite Learning-Based Mutant Particle Swarm Optimization.

Computational intelligence and neuroscience
Multiple-input and multiple-output (MIMO) technology is one of the latest technologies to enhance the capacity of the channel as well as the service quality of the communication system. By using the MIMO technology at the physical layer, the estimati...

Machine Learning Methods as a Tool for Predicting Risk of Illness Applying Next-Generation Sequencing Data.

Risk analysis : an official publication of the Society for Risk Analysis
Next-generation sequencing (NGS) data present an untapped potential to improve microbial risk assessment (MRA) through increased specificity and redefinition of the hazard. Most of the MRA models do not account for differences in survivability and vi...

Machine learning in critical care: state-of-the-art and a sepsis case study.

Biomedical engineering online
BACKGROUND: Like other scientific fields, such as cosmology, high-energy physics, or even the life sciences, medicine and healthcare face the challenge of an extremely quick transformation into data-driven sciences. This challenge entails the dauntin...

Deep CNNs with Robust LBP Guiding Pooling for Face Recognition.

Sensors (Basel, Switzerland)
Pooling layer in Convolutional Neural Networks (CNNs) is designed to reduce dimensions and computational complexity. Unfortunately, CNN is easily disturbed by noise in images when extracting features from input images. The traditional pooling layer d...

A 0.086-mm 12.7-pJ/SOP 64k-Synapse 256-Neuron Online-Learning Digital Spiking Neuromorphic Processor in 28-nm CMOS.

IEEE transactions on biomedical circuits and systems
Shifting computing architectures from von Neumann to event-based spiking neural networks (SNNs) uncovers new opportunities for low-power processing of sensory data in applications such as vision or sensorimotor control. Exploring roads toward cogniti...

Accelerating deep learning with memcomputing.

Neural networks : the official journal of the International Neural Network Society
Restricted Boltzmann machines (RBMs) and their extensions, often called "deep-belief networks", are powerful neural networks that have found applications in the fields of machine learning and artificial intelligence. The standard way to train these m...

Generalized multifactor dimensionality reduction approaches to identification of genetic interactions underlying ordinal traits.

Genetic epidemiology
The manifestation of complex traits is influenced by gene-gene and gene-environment interactions, and the identification of multifactor interactions is an important but challenging undertaking for genetic studies. Many complex phenotypes such as dise...