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Precision screening for familial hypercholesterolaemia: a machine learning study applied to electronic health encounter data.

The Lancet. Digital health
BACKGROUND: Cardiovascular outcomes for people with familial hypercholesterolaemia can be improved with diagnosis and medical management. However, 90% of individuals with familial hypercholesterolaemia remain undiagnosed in the USA. We aimed to accel...

Deep learning in the detection of high-grade glioma recurrence using multiple MRI sequences: A pilot study.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
The identification of high-grade glioma (HGG) progression may pose a diagnostic dilemma due to similar appearances of treatment-related changes (TRC) (e.g. pseudoprogression or radionecrosis). Deep learning (DL) may be able to assist with this task. ...

Discrimination of the behavioural dynamics of visually impaired infants via deep learning.

Nature biomedical engineering
Sensory loss is associated with behavioural changes, but how behavioural dynamics change when a sensory modality is impaired remains unclear. Here, by recording under a designed standardized scenario, the behavioural phenotypes of 4,196 infants who e...

The FluPRINT dataset, a multidimensional analysis of the influenza vaccine imprint on the immune system.

Scientific data
Machine learning has the potential to identify novel biological factors underlying successful antibody responses to influenza vaccines. The first attempts have revealed a high level of complexity in establishing influenza immunity, and many different...

An Exploration of Machine Learning Methods for Robust Boredom Classification Using EEG and GSR Data.

Sensors (Basel, Switzerland)
In recent years, affective computing has been actively researched to provide a higher level of emotion-awareness. Numerous studies have been conducted to detect the user's emotions from physiological data. Among a myriad of target emotions, boredom, ...

A Cascaded Convolutional Neural Network for Assessing Signal Quality of Dynamic ECG.

Computational and mathematical methods in medicine
Motion artifacts and myoelectrical noise are common issues complicating the collection and processing of dynamic electrocardiogram (ECG) signals. Recent signal quality studies have utilized a binary classification metric in which ECG samples are dete...

Clinical Significance of Circulating Cardiomyocyte-Specific Cell-Free DNA in Patients With Heart Failure: A Proof-of-Concept Study.

The Canadian journal of cardiology
We investigated clinical significance of cell-free DNA (cfDNA) in heart failure. This study enrolled 32 heart failure patients and 28 control subjects. Total cfDNA levels were not different between groups (P = 0.343). Bisulfite-digital polymerase cha...

The diagnosticity of psychophysiological signatures: Can we disentangle mental workload from acute stress with ECG and fNIRS?

International journal of psychophysiology : official journal of the International Organization of Psychophysiology
The ability to identify reliable and sensitive physiological signatures of psychological dimensions is key to developing intelligent adaptive systems that may in turn help to mitigate human error in complex operations. The challenge of this endeavor ...

A speckle-tracking strain-based artificial neural network model to differentiate cardiomyopathy type.

Scandinavian cardiovascular journal : SCJ
In heart failure, invasive angiography is often employed to differentiate ischaemic from non-ischaemic cardiomyopathy. We aim to examine the predictive value of echocardiographic strain features alone and in combination with other features to differ...

Investigating Risk Factors and Predicting Complications in Deep Brain Stimulation Surgery with Machine Learning Algorithms.

World neurosurgery
BACKGROUND: Deep brain stimulation (DBS) surgery is an option for patients experiencing medically resistant neurologic symptoms. DBS complications are rare; finding significant predictors requires a large number of surgeries. Machine learning algorit...