AI Medical Compendium Topic:
Supervised Machine Learning

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Exploring supervised machine learning approaches to predicting Veterans Health Administration chiropractic service utilization.

Chiropractic & manual therapies
BACKGROUND: Chronic spinal pain conditions affect millions of US adults and carry a high healthcare cost burden, both direct and indirect. Conservative interventions for spinal pain conditions, including chiropractic care, have been associated with l...

Multi-class motor imagery EEG classification using collaborative representation-based semi-supervised extreme learning machine.

Medical & biological engineering & computing
Both labeled and unlabeled data have been widely used in electroencephalographic (EEG)-based brain-computer interface (BCI). However, labeled EEG samples are generally scarce and expensive to collect, while unlabeled samples are considered to be abun...

A deep learning approach for magnetization transfer contrast MR fingerprinting and chemical exchange saturation transfer imaging.

NeuroImage
Semisolid magnetization transfer contrast (MTC) and chemical exchange saturation transfer (CEST) MRI based on MT phenomenon have shown potential to evaluate brain development, neurological, psychiatric, and neurodegenerative diseases. However, a qual...

Rapid detection of microbiota cell type diversity using machine-learned classification of flow cytometry data.

Communications biology
The study of complex microbial communities typically entails high-throughput sequencing and downstream bioinformatics analyses. Here we expand and accelerate microbiota analysis by enabling cell type diversity quantification from multidimensional flo...

A supervised learning framework for chromatin loop detection in genome-wide contact maps.

Nature communications
Accurately predicting chromatin loops from genome-wide interaction matrices such as Hi-C data is critical to deepening our understanding of proper gene regulation. Current approaches are mainly focused on searching for statistically enriched dots on ...

A semi-supervised approach for extracting TCM clinical terms based on feature words.

BMC medical informatics and decision making
BACKGROUND: A semi-supervised model is proposed for extracting clinical terms of Traditional Chinese Medicine using feature words.

Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition.

Methods (San Diego, Calif.)
In the last decade, machine learning and artificial intelligence applications have received a significant boost in performance and attention in both academic research and industry. The success behind most of the recent state-of-the-art methods can be...

Self-supervised learning of physics-guided reconstruction neural networks without fully sampled reference data.

Magnetic resonance in medicine
PURPOSE: To develop a strategy for training a physics-guided MRI reconstruction neural network without a database of fully sampled data sets.

Inferring transportation mode from smartphone sensors: Evaluating the potential of Wi-Fi and Bluetooth.

PloS one
Understanding which transportation modes people use is critical for smart cities and planners to better serve their citizens. We show that using information from pervasive Wi-Fi access points and Bluetooth devices can enhance GPS and geographic infor...