AIMC Topic: Machine Learning

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Combined Machine Learning and Molecular Modelling Workflow for the Recognition of Potentially Novel Fungicides.

Molecules (Basel, Switzerland)
Novel machine learning and molecular modelling filtering procedures for drug repurposing have been carried out for the recognition of the novel fungicide targets of Cyp51 and Erg2. Classification and regression approaches on molecular descriptors hav...

Digital conversations about suicide among teenagers and adults with epilepsy: A big-data, machine learning analysis.

Epilepsia
OBJECTIVE: Digital media conversations can provide important insight into the concerns and struggles of people with epilepsy (PWE) outside of formal clinical settings and help generate useful information for treatment planning. Our study aimed to exp...

MRI radiomics-based machine-learning classification of bone chondrosarcoma.

European journal of radiology
PURPOSE: To evaluate the diagnostic performance of machine learning for discrimination between low-grade and high-grade cartilaginous bone tumors based on radiomic parameters extracted from unenhanced magnetic resonance imaging (MRI).

Measurement and identification of mental workload during simulated computer tasks with multimodal methods and machine learning.

Ergonomics
This study attempted to multimodally measure mental workload and validate indicators for estimating mental workload. A simulated computer work composed of mental arithmetic tasks with different levels of difficulty was designed and used in the experi...

Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic resonance imaging.

Advances in rheumatology (London, England)
BACKGROUND: Currently, magnetic resonance imaging (MRI) is used to evaluate active inflammatory sacroiliitis related to axial spondyloarthritis (axSpA). The qualitative and semiquantitative diagnosis performed by expert radiologists and rheumatologis...

Generation and evaluation of synthetic patient data.

BMC medical research methodology
BACKGROUND: Machine learning (ML) has made a significant impact in medicine and cancer research; however, its impact in these areas has been undeniably slower and more limited than in other application domains. A major reason for this has been the la...

A Method to Extract Feature Variables Contributed in Nonlinear Machine Learning Prediction.

Methods of information in medicine
BACKGROUND: Although advances in prediction accuracy have been made with new machine learning methods, such as support vector machines and deep neural networks, these methods make nonlinear machine learning models and thus lack the ability to explain...

A review of modern technologies for tackling COVID-19 pandemic.

Diabetes & metabolic syndrome
OBJECTIVE: Science and technology sector constituting of data science, machine learning and artificial intelligence are contributing towards COVID-19. The aim of the present study is to discuss the various aspects of modern technology used to fight a...

Developing a novel force forecasting technique for early prediction of critical events in robotics.

PloS one
Safety critical events in robotic applications can often be characterized by forces between the robot end-effector and the environment. One application in which safe interaction between the robot and environment is critical is in the area of medical ...

Big Data and Atrial Fibrillation: Current Understanding and New Opportunities.

Journal of cardiovascular translational research
Atrial fibrillation (AF) is the most common arrhythmia with diverse etiology that remarkably relates to high morbidity and mortality. With the advancements in intensive clinical and basic research, the understanding of electrophysiological and pathop...