AIMC Topic: Time Factors

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Event-Triggered Synchronization Strategy for Multiple Neural Networks With Time Delay.

IEEE transactions on cybernetics
This paper deals with global exponential synchronization of multiple neural networks (NNs) with time delay via a very broad class of event-triggered coupling, in which coupling matrix can be non-Laplacian. Some simple and convenient sufficient condit...

Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: Tumors are continuously evolving biological systems, and medical imaging is uniquely positioned to monitor changes throughout treatment. Although qualitatively tracking lesions over space and time may be trivial, the development of clinicall...

Evaluate driver response to active warning system in level-2 automated vehicles.

Accident; analysis and prevention
As vehicles with automated functions become more prevalent on U.S. roadways, maintaining driver attention while the vehicle is engaged in automation will be an important consideration for safe operation of these vehicles. The objective of this paper ...

Refining humane endpoints in mouse models of disease by systematic review and machine learning-based endpoint definition.

ALTEX
Ideally, humane endpoints allow for early termination of experiments by minimizing an animal's discomfort, distress and pain, while ensuring that scientific objectives are reached. Yet, lack of commonly agreed methodology and heterogeneity of cut-off...

Temporal stability assessment in shear wave elasticity images validated by deep learning neural network for chronic liver disease fibrosis stage assessment.

Medical physics
PURPOSE: To automatically detect and isolate areas of low and high stiffness temporal stability in shear wave elastography (SWE) image sequences and define their impact in chronic liver disease (CLD) diagnosis improvement by means of clinical examina...

Machine learning performance in a microbial molecular autopsy context: A cross-sectional postmortem human population study.

PloS one
BACKGROUND: The postmortem microbiome can provide valuable information to a death investigation and to the human health of the once living. Microbiome sequencing produces, in general, large multi-dimensional datasets that can be difficult to analyze ...

Fast and reliable determination of Escherichia coli susceptibility to antibiotics: Infrared microscopy in tandem with machine learning algorithms.

Journal of biophotonics
Antimicrobial drugs have an important role in controlling bacterial infectious diseases. However, the increasing resistance of bacteria to antibiotics has become a global health care problem. Rapid determination of antimicrobial susceptibility of cli...

An Equation Based on Fuzzy Mathematics to Assess the Timing of Haemodialysis Initiation.

Scientific reports
In order to develop an equation that integrates multiple clinical factors including signs and symptoms associated with uraemia to assess the initiation of dialysis, we conducted a retrospective cohort study including 25 haemodialysis centres in Mainl...

Dynamic multi-outcome prediction after injury: Applying adaptive machine learning for precision medicine in trauma.

PloS one
OBJECTIVE: Machine learning techniques have demonstrated superior discrimination compared to conventional statistical approaches in predicting trauma death. The objective of this study is to evaluate whether machine learning algorithms can be used to...

Identifying Factors That Affect Patient Survival After Orthotopic Liver Transplant Using Machine-Learning Techniques.

Experimental and clinical transplantation : official journal of the Middle East Society for Organ Transplantation
OBJECTIVES: Survival after liver transplant depends on pretransplant, peritransplant, and posttransplant factors. Identifying effective factors for patient survival after transplant can help transplant centers make better decisions.