AIMC Topic: Machine Learning

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Development and validation of an interpretable machine learning model for predicting post-stroke epilepsy.

Epilepsy research
BACKGROUND: Epilepsy is a serious complication after an ischemic stroke. Although two studies have developed prediction model for post-stroke epilepsy (PSE), their accuracy remains insufficient, and their applicability to different populations is unc...

APLpred: A machine learning-based tool for accurate prediction and characterization of asparagine peptide lyases using sequence-derived optimal features.

Methods (San Diego, Calif.)
Asparagine peptide lyase (APL) is among the seven groups of proteases, also known as proteolytic enzymes, which are classified according to their catalytic residue. APLs are synthesized as precursors or propeptides that undergo self-cleavage through ...

Using machine learning to automatically measure kyphotic and lordotic angle measurements on radiographs for children with adolescent idiopathic scoliosis.

Medical engineering & physics
Measuring the kyphotic angle (KA) and lordotic angle (LA) on lateral radiographs is important to truly diagnose children with adolescent idiopathic scoliosis. However, it is a time-consuming process to measure the KA because the endplate of the upper...

Meta-heuristic optimization algorithms based feature selection for joint moment prediction of sit-to-stand movement using machine learning algorithms.

Computers in biology and medicine
The sit-to-stand (STS) movement is fundamental in daily activities, involving coordinated motion of the lower extremities and trunk, which leads to the generation of joint moments based on joint angles and limb properties. Traditional methods for det...

Artificial Intelligence and Multiple Sclerosis.

Current neurology and neuroscience reports
In this paper, we analyse the different advances in artificial intelligence (AI) approaches in multiple sclerosis (MS). AI applications in MS range across investigation of disease pathogenesis, diagnosis, treatment, and prognosis. A subset of AI, Mac...

Prediction of prognosis in patients with systemic sclerosis based on a machine-learning model.

Clinical rheumatology
OBJECTIVE: The clinical manifestations of systemic sclerosis (SSc) are highly variable, resulting in varied outcomes and complications. Diverse fibrosis of the skin and internal organs, vasculopathy, and dysregulated immune system lead to poor and va...

Prediction of Vacuum Ultraviolet/Ultraviolet Gas-Phase Absorption Spectra Using Molecular Feature Representations and Machine Learning.

Journal of chemical information and modeling
Ultraviolet (UV) absorption spectroscopy is a widely used tool for quantitative and qualitative analyses of chemical compounds. In the gas phase, vacuum UV (VUV) and UV absorption spectra are specific and diagnostic for many small molecules. An accur...

Plasma infrared fingerprinting with machine learning enables single-measurement multi-phenotype health screening.

Cell reports. Medicine
Infrared spectroscopy is a powerful technique for probing the molecular profiles of complex biofluids, offering a promising avenue for high-throughput in vitro diagnostics. While several studies showcased its potential in detecting health conditions,...

Elbow Gesture Recognition with an Array of Inductive Sensors and Machine Learning.

Sensors (Basel, Switzerland)
This work presents a novel approach for elbow gesture recognition using an array of inductive sensors and a machine learning algorithm (MLA). This paper describes the design of the inductive sensor array integrated into a flexible and wearable sleeve...

Wearable ECG Device and Machine Learning for Heart Monitoring.

Sensors (Basel, Switzerland)
With cardiovascular diseases (CVD) remaining a leading cause of mortality, wearable devices for monitoring cardiac activity have gained significant, renewed interest among the medical community. This paper introduces an innovative ECG monitoring syst...