AIMC Topic: Machine Learning

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Discovery of Parkinson's disease states and disease progression modelling: a longitudinal data study using machine learning.

The Lancet. Digital health
BACKGROUND: Parkinson's disease is heterogeneous in symptom presentation and progression. Increased understanding of both aspects can enable better patient management and improve clinical trial design. Previous approaches to modelling Parkinson's dis...

A Conference-Friendly, Hands-on Introduction to Deep Learning for Radiology Trainees.

Journal of digital imaging
Artificial or augmented intelligence, machine learning, and deep learning will be an increasingly important part of clinical practice for the next generation of radiologists. It is therefore critical that radiology residents develop a practical under...

Neuroinflammation and Alzheimer's Disease: A Machine Learning Approach to CSF Proteomics.

Cells
In Alzheimer's disease (AD), the contribution of pathophysiological mechanisms other than amyloidosis and tauopathy is now widely recognized, although not clearly quantifiable by means of fluid biomarkers. We aimed to identify quantifiable protein bi...

Affective Computing on Machine Learning-Based Emotion Recognition Using a Self-Made EEG Device.

Sensors (Basel, Switzerland)
In this research, we develop an affective computing method based on machine learning for emotion recognition using a wireless protocol and a wearable electroencephalography (EEG) custom-designed device. The system collects EEG signals using an eight-...

Machine learning to predict distal caries in mandibular second molars associated with impacted third molars.

Scientific reports
Impacted mandibular third molars (M3M) are associated with the occurrence of distal caries on the adjacent mandibular second molars (DCM2M). In this study, we aimed to develop and validate five machine learning (ML) models designed to predict the occ...

Decision-Making Model of Product Modeling Big Data Design Scheme Based on Neural Network Optimized by Genetic Algorithm.

Computational intelligence and neuroscience
At present, machine learning artificial neural network technology, as one of the core technologies of enterprises, has received unprecedented attention. This technology is widely used in automatic driving, pattern recognition, teaching aid, product m...

Usages of Spark Framework with Different Machine Learning Algorithms.

Computational intelligence and neuroscience
Sensors, satellites, mobile devices, social media, e-commerce, and the Internet, among others, saturate us with data. The Internet of Things, in particular, enables massive amounts of data to be generated more quickly. The Internet of Things is a ter...

Utilizing machine learning with knockoff filtering to extract significant metabolites in Crohn's disease with a publicly available untargeted metabolomics dataset.

PloS one
Metabolomic data processing pipelines have been improving in recent years, allowing for greater feature extraction and identification. Lately, machine learning and robust statistical techniques to control false discoveries are being incorporated into...

Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach.

PloS one
Interpersonal violence (IPV) is highly prevalent in the United States and is a major public health problem. The emergence and/or worsening of chronic pain are known sequelae of IPV; however, not all those who experience IPV develop chronic pain. To m...

Development of a novel noninvasive quantitative method to monitor Siraitia grosvenorii cell growth and browning degree using an integrated computer-aided vision technology and machine learning.

Biotechnology and bioengineering
The rapid, accurate and noninvasive detection of biomass and plant cell browning can provide timely feedback on cell growth in plant cell culture. In this study, Siraitia grosvenorii suspension cells were taken as an example, a phenotype analysis pla...