AIMC Topic: Machine Learning

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Using machine learning to investigate self-medication purchasing in England via high street retailer loyalty card data.

PloS one
The availability alongside growing awareness of medicine has led to increased self-treatment of minor ailments. Self-medication is where one 'self' diagnoses and prescribes over the counter medicines for treatment. The self-care movement has importan...

Detecting abnormal electroencephalograms using deep convolutional networks.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVES: Electroencephalography (EEG) is a central part of the medical evaluation for patients with neurological disorders. Training an algorithm to label the EEG normal vs abnormal seems challenging, because of EEG heterogeneity and dependence of...

A machine-learning approach to volitional control of a closed-loop deep brain stimulation system.

Journal of neural engineering
OBJECTIVE: Deep brain stimulation (DBS) is a well-established treatment for essential tremor, but may not be an optimal therapy, as it is always on, regardless of symptoms. A closed-loop (CL) DBS, which uses a biosignal to determine when stimulation ...

MAIA-A machine learning assisted image annotation method for environmental monitoring and exploration.

PloS one
Digital imaging has become one of the most important techniques in environmental monitoring and exploration. In the case of the marine environment, mobile platforms such as autonomous underwater vehicles (AUVs) are now equipped with high-resolution c...

Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging.

European radiology
OBJECTIVES: Preoperative, noninvasive prediction of the meningioma grade is important because it influences the treatment strategy. The purpose of this study was to evaluate the role of radiomics features of postcontrast T1-weighted images (T1C), app...

Machine learning combined with non-targeted LC-HRMS analysis for a risk warning system of chemical hazards in drinking water: A proof of concept.

Talanta
Guaranteeing clean drinking water to the global population is becoming more challenging, because of the cases of water scarcity across the globe, growing population, and increased chemical footprint of this population. Existing targeted strategies fo...

Machine learning of brain gray matter differentiates sex in a large forensic sample.

Human brain mapping
Differences between males and females have been extensively documented in biological, psychological, and behavioral domains. Among these, sex differences in the rate and typology of antisocial behavior remains one of the most conspicuous and enduring...

Retinal image analytics detects white matter hyperintensities in healthy adults.

Annals of clinical and translational neurology
OBJECTIVE: We investigated whether an automatic retinal image analysis (ARIA) incorporating machine learning approach can identify asymptomatic older adults harboring high burden of white matter hyperintensities (WMH) using MRI as gold standard.

Defining and Predicting Pain Volatility in Users of the Manage My Pain App: Analysis Using Data Mining and Machine Learning Methods.

Journal of medical Internet research
BACKGROUND: Measuring and predicting pain volatility (fluctuation or variability in pain scores over time) can help improve pain management. Perceptions of pain and its consequent disabling effects are often heightened under the conditions of greater...

Machine learning algorithms for activity recognition in ambulant children and adolescents with cerebral palsy.

Journal of neuroengineering and rehabilitation
BACKGROUND: Cerebral palsy (CP) is the most common physical disability among children (2.5 to 3.6 cases per 1000 live births). Inadequate physical activity (PA) is a major problem effecting the health and well-being of children with CP. Practical, ye...