AIMC Topic: Machine Learning

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Machine learning defined diagnostic criteria for differentiating pituitary metastasis from autoimmune hypophysitis in patients undergoing immune checkpoint blockade therapy.

European journal of cancer (Oxford, England : 1990)
PURPOSE: New-onset pituitary gland lesions are observed in up to 18% of cancer patients undergoing treatment with immune checkpoint blockers (ICB). We aimed to develop and validate an imaging-based decision-making algorithm for use by the clinician t...

BindSpace decodes transcription factor binding signals by large-scale sequence embedding.

Nature methods
The decoding of transcription factor (TF) binding signals in genomic DNA is a fundamental problem. Here we present a prediction model called BindSpace that learns to embed DNA sequences and TF labels into the same space. By training on binding data f...

Locomotion Mode Recognition With Robotic Transtibial Prosthesis in Inter-Session and Inter-Day Applications.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Locomotion mode recognition across multiple sessions and days is an indispensable step towards the practical use of the robotic transtibial prosthesis. In this study, we proposed an adaptive recognition strategy to against the time-varying features o...

Neural-network classification of cardiac disease from P cardiovascular magnetic resonance spectroscopy measures of creatine kinase energy metabolism.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: The heart's energy demand per gram of tissue is the body's highest and creatine kinase (CK) metabolism, its primary energy reserve, is compromised in common heart diseases. Here, neural-network analysis is used to test whether noninvasive...

A modeling and machine learning approach to ECG feature engineering for the detection of ischemia using pseudo-ECG.

PloS one
Early detection of coronary heart disease (CHD) has the potential to prevent the millions of deaths that this disease causes worldwide every year. However, there exist few automatic methods to detect CHD at an early stage. A challenge in the developm...

Training machine learning models to predict 30-day mortality in patients discharged from the emergency department: a retrospective, population-based registry study.

BMJ open
OBJECTIVES: The aim of this work was to train machine learning models to identify patients at end of life with clinically meaningful diagnostic accuracy, using 30-day mortality in patients discharged from the emergency department (ED) as a proxy.

Constructing a Personalized Cross-Day EEG-Based Emotion-Classification Model Using Transfer Learning.

IEEE journal of biomedical and health informatics
State-of-the-art electroencephalogram (EEG)-based emotion-classification works indicate that a personalized model may not be well exploited until sufficient labeled data are available, given a substantial EEG non-stationarity over days. However, it i...

Toward a Better Estimation of Functional Brain Network for Mild Cognitive Impairment Identification: A Transfer Learning View.

IEEE journal of biomedical and health informatics
Mild cognitive impairment (MCI) is an intermediate stage of brain cognitive decline, associated with increasing risk of developing Alzheimer's disease (AD). It is believed that early treatment of MCI could slow down the progression of AD, and functio...

Automated sleep scoring: A review of the latest approaches.

Sleep medicine reviews
Clinical sleep scoring involves a tedious visual review of overnight polysomnograms by a human expert, according to official standards. It could appear then a suitable task for modern artificial intelligence algorithms. Indeed, machine learning algor...

Clustering Neural Patterns in Kernel Reinforcement Learning Assists Fast Brain Control in Brain-Machine Interfaces.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Neuroprosthesis enables the brain control on the external devices purely using neural activity for paralyzed people. Supervised learning decoders recalibrate or re-fit the discrepancy between the desired target and decoder's output, where the correct...