AIMC Topic: Machine Learning

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Machine learning predicts lymph node metastasis of poorly differentiated-type intramucosal gastric cancer.

Scientific reports
To construct a machine learning algorithm model of lymph node metastasis (LNM) in patients with poorly differentiated-type intramucosal gastric cancer. 1169 patients with postoperative gastric cancer were divided into a training group and a test grou...

Face Hallucination With Finishing Touches.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Obtaining a high-quality frontal face image from a low-resolution (LR) non-frontal face image is primarily important for many facial analysis applications. However, mainstreams either focus on super-resolving near-frontal LR faces or frontalizing non...

Use of Machine Learning to Re-Assess Patterns of Multivariate Functional Recovery after Fluid Percussion Injury: Operation Brain Trauma Therapy.

Journal of neurotrauma
Traumatic brain injury (TBI) is a leading cause of death and disability. Yet, despite immense research efforts, treatment options remain elusive. Translational failures in TBI are often attributed to the heterogeneity of the TBI population and limite...

Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission.

Scientific reports
The aim of this study was to develop a predictive model of pediatric mortality in the early stages of intensive care unit (ICU) admission using machine learning. Patients less than 18 years old who were admitted to ICUs at four tertiary referral hosp...

Optimization of probiotic therapeutics using machine learning in an artificial human gastrointestinal tract.

Scientific reports
The gut microbiota's metabolome is composed of bioactive metabolites that confer disease resilience. Probiotics' therapeutic potential hinges on their metabolome altering ability; however, characterizing probiotics' metabolic activity remains a formi...

Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning.

Nature communications
Recent critical commentaries unfavorably compare deep learning (DL) with standard machine learning (SML) approaches for brain imaging data analysis. However, their conclusions are often based on pre-engineered features depriving DL of its main advant...

Current cancer driver variant predictors learn to recognize driver genes instead of functional variants.

BMC biology
BACKGROUND: Identifying variants that drive tumor progression (driver variants) and distinguishing these from variants that are a byproduct of the uncontrolled cell growth in cancer (passenger variants) is a crucial step for understanding tumorigenes...

Modelization of Covid-19 pandemic spreading: A machine learning forecasting with relaxation scenarios of countermeasures.

Journal of infection and public health
BACKGROUND & OBJECTIVE: Mathematical modeling is the most scientific technique to understand the evolution of natural phenomena, including the spread of infectious diseases. Therefore, these modeling tools have been widely used in epidemiology for pr...

Creating and concentrating quantum resource states in noisy environments using a quantum neural network.

Neural networks : the official journal of the International Neural Network Society
Quantum information processing tasks require exotic quantum states as a prerequisite. They are usually prepared with many different methods tailored to the specific resource state. Here we provide a versatile unified state preparation scheme based on...

Elbow Motion Trajectory Prediction Using a Multi-Modal Wearable System: A Comparative Analysis of Machine Learning Techniques.

Sensors (Basel, Switzerland)
Motion intention detection is fundamental in the implementation of human-machine interfaces applied to assistive robots. In this paper, multiple machine learning techniques have been explored for creating upper limb motion prediction models, which ge...