AIMC Topic: Time Factors

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Delta activity encodes taste information in the human brain.

NeuroImage
The categorization of food via sensing nutrients or toxins is crucial to the survival of any organism. On ingestion, rapid responses within the gustatory system are required to identify the oral stimulus to guide immediate behavior (swallowing or exp...

Controlling an organic synthesis robot with machine learning to search for new reactivity.

Nature
The discovery of chemical reactions is an inherently unpredictable and time-consuming process. An attractive alternative is to predict reactivity, although relevant approaches, such as computer-aided reaction design, are still in their infancy. React...

Deep learning models for bacteria taxonomic classification of metagenomic data.

BMC bioinformatics
BACKGROUND: An open challenge in translational bioinformatics is the analysis of sequenced metagenomes from various environmental samples. Of course, several studies demonstrated the 16S ribosomal RNA could be considered as a barcode for bacteria cla...

Development and Application of a Machine Learning Approach to Assess Short-term Mortality Risk Among Patients With Cancer Starting Chemotherapy.

JAMA network open
IMPORTANCE: Patients with cancer who die soon after starting chemotherapy incur costs of treatment without the benefits. Accurately predicting mortality risk before administering chemotherapy is important, but few patient data-driven tools exist.

An Arcak-type state estimation design for time-delayed static neural networks with leakage term based on unified criteria.

Neural networks : the official journal of the International Neural Network Society
The issue of unified dissipativity-based Arcak-type state estimator design for delayed static neural networks (SNNs) with leakage term and noise distraction was considered here. An Arcak-type state observer, which is compact than the usually used Lue...

Assessment of the Feasibility of automated, real-time clinical decision support in the emergency department using electronic health record data.

BMC emergency medicine
BACKGROUND: The use of big data and machine learning within clinical decision support systems (CDSSs) has the potential to transform medicine through better prognosis, diagnosis and automation of tasks. Real-time application of machine learning algor...

Optimization of goose breast meat tenderness by rapid ultrasound treatment using response surface methodology and artificial neural network.

Animal science journal = Nihon chikusan Gakkaiho
The aim of this study was to develop a prediction model on tenderization of goose breast meat by response surface methodology (RSM) and artificial neural network (ANN). The experiments were operated on the basis of a three-level, three-variable (ultr...

Bioimpedance and New-Onset Heart Failure: A Longitudinal Study of >500 000 Individuals From the General Population.

Journal of the American Heart Association
BACKGROUND: Heart failure constitutes a high burden on patients and society, but although lifetime risk is high, it is difficult to predict without costly or invasive testing. We aimed to establish new risk factors of heart failure, which potentially...

An Algorithm Based on Deep Learning for Predicting In-Hospital Cardiac Arrest.

Journal of the American Heart Association
BACKGROUND: In-hospital cardiac arrest is a major burden to public health, which affects patient safety. Although traditional track-and-trigger systems are used to predict cardiac arrest early, they have limitations, with low sensitivity and high fal...

Time-Varying EEG Correlations Improve Automated Neonatal Seizure Detection.

International journal of neural systems
The aim of this study was to develop methods for detecting the nonstationary periodic characteristics of neonatal electroencephalographic (EEG) seizures by adapting estimates of the correlation both in the time (spike correlation; SC) and time-freque...