AIMC Topic: Machine Learning

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Differentiating Epileptic and Psychogenic Non-Epileptic Seizures Using Machine Learning Analysis of EEG Plot Images.

Sensors (Basel, Switzerland)
The treatment of epilepsy, the second most common chronic neurological disorder, is often complicated by the failure of patients to respond to medication. Treatment failure with anti-seizure medications is often due to the presence of non-epileptic s...

Preclinical identification of acute coronary syndrome without high sensitivity troponin assays using machine learning algorithms.

Scientific reports
Preclinical management of patients with acute chest pain and their identification as candidates for urgent coronary revascularization without the use of high sensitivity troponin essays remains a critical challenge in emergency medicine. We enrolled ...

A machine-learning method isolating changes in wrist kinematics that identify age-related changes in arm movement.

Scientific reports
Normal aging often results in an increase in physiological tremors and slowing of the movement of the hands, which can impair daily activities and quality of life. This study, using lightweight wearable non-invasive sensors, aimed to detect and ident...

Machine learning approach for recognition and morphological analysis of isolated astrocytes in phase contrast microscopy.

Scientific reports
Astrocytes are glycolytically active cells in the central nervous system playing a crucial role in various brain processes from homeostasis to neurotransmission. Astrocytes possess a complex branched morphology, frequently examined by fluorescent mic...

Exploring inertial sensor-based balance biomarkers for early detection of mild cognitive impairment.

Scientific reports
Dementia is characterized by a progressive loss of cognitive abilities, and diagnosing its early stages Mild Cognitive Impairment (MCI), is difficult since it is a transitory state that is different from total cognitive collapse. Recent clinical rese...

Machine learning assists prediction of genes responsible for plant specialized metabolite biosynthesis by integrating multi-omics data.

BMC genomics
BACKGROUND: Plant specialized (or secondary) metabolites (PSM), also known as phytochemicals, natural products, or plant constituents, play essential roles in interactions between plants and environment. Although many research efforts have focused on...

Paper spray mass spectrometry combined with machine learning as a rapid diagnostic for chronic kidney disease.

The Analyst
A new analytical method for chronic kidney disease (CKD) detection utilizing paper spray mass spectrometry (PS-MS) combined with machine learning is presented. The analytical protocol is rapid and simple, based on metabolic profile alterations in uri...

Ensemble learning and ground-truth validation of synaptic connectivity inferred from spike trains.

PLoS computational biology
Probing the architecture of neuronal circuits and the principles that underlie their functional organization remains an important challenge of modern neurosciences. This holds true, in particular, for the inference of neuronal connectivity from large...

UNNT: A novel Utility for comparing Neural Net and Tree-based models.

PLoS computational biology
The use of deep learning (DL) is steadily gaining traction in scientific challenges such as cancer research. Advances in enhanced data generation, machine learning algorithms, and compute infrastructure have led to an acceleration in the use of deep ...

The role of machine learning in advancing diabetic foot: a review.

Frontiers in endocrinology
BACKGROUND: Diabetic foot complications impose a significant strain on healthcare systems worldwide, acting as a principal cause of morbidity and mortality in individuals with diabetes mellitus. While traditional methods in diagnosing and treating th...