AIMC Topic: Humans

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Prediction of fatty liver disease using machine learning algorithms.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Fatty liver disease (FLD) is a common clinical complication; it is associated with high morbidity and mortality. However, an early prediction of FLD patients provides an opportunity to make an appropriate strategy for preven...

A propagation-DNN: Deep combination learning of multi-level features for MR prostate segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Prostate segmentation on Magnetic Resonance (MR) imaging is problematic because disease changes the shape and boundaries of the gland and it can be difficult to separate the prostate from surrounding tissues. We propose an a...

Use of Machine Learning to Identify Follow-Up Recommendations in Radiology Reports.

Journal of the American College of Radiology : JACR
PURPOSE: The aims of this study were to assess follow-up recommendations in radiology reports, develop and assess traditional machine learning (TML) and deep learning (DL) models in identifying follow-up, and benchmark them against a natural language...

Machine learning for detecting moyamoya disease in plain skull radiography using a convolutional neural network.

EBioMedicine
BACKGROUND: Recently, innovative attempts have been made to identify moyamoya disease (MMD) by focusing on the morphological differences in the head of MMD patients. Following the recent revolution in the development of deep learning (DL) algorithms,...

A wearable vibrotactile system for distributed guidance in teleoperation and virtual environments.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
A novel wearable vibrotactile system is proposed in this article to enhance the performance of teleoperation robot systems. Using a wearable vibrotactile glove, the proposed system guides the operator in the master-slave control through a vibrotactil...

A Single Session of Robot-Controlled Proprioceptive Training Modulates Functional Connectivity of Sensory Motor Networks and Improves Reaching Accuracy in Chronic Stroke.

Neurorehabilitation and neural repair
BACKGROUND: Passive robot-generated arm movements in conjunction with proprioceptive decision making and feedback modulate functional connectivity (FC) in sensory motor networks and improve sensorimotor adaptation in normal individuals. This proof-of...

Model based on GA and DNN for prediction of mRNA-Smad7 expression regulated by miRNAs in breast cancer.

Theoretical biology & medical modelling
BACKGROUND: The Smad7 protein is negative regulator of the TGF-β signaling pathway, which is upregulated in patients with breast cancer. miRNAs regulate proteins expressions by arresting or degrading the mRNAs. The purpose of this work is to identify...

Machine learning methodologies versus cardiovascular risk scores, in predicting disease risk.

BMC medical research methodology
BACKGROUND: The use of Cardiovascular Disease (CVD) risk estimation scores in primary prevention has long been established. However, their performance still remains a matter of concern. The aim of this study was to explore the potential of using ML m...

Primary Sclerosing Cholangitis Risk Estimate Tool (PREsTo) Predicts Outcomes of the Disease: A Derivation and Validation Study Using Machine Learning.

Hepatology (Baltimore, Md.)
Improved methods are needed to risk stratify and predict outcomes in patients with primary sclerosing cholangitis (PSC). Therefore, we sought to derive and validate a prediction model and compare its performance to existing surrogate markers. The mod...

Covariance Matrix Adaptation for Multiobjective Multiarmed Bandits.

IEEE transactions on neural networks and learning systems
Upper confidence bound (UCB) is a successful multiarmed bandit for regret minimization. The covariance matrix adaptation (CMA) for Pareto UCB (CMA-PUCB) algorithm considers stochastic reward vectors with correlated objectives. We upper bound the cumu...