AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Data Analysis

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Use of machine learning to analyse routinely collected intensive care unit data: a systematic review.

Critical care (London, England)
BACKGROUND: Intensive care units (ICUs) face financial, bed management, and staffing constraints. Detailed data covering all aspects of patients' journeys into and through intensive care are now collected and stored in electronic health records: mach...

Automated sleep scoring: A review of the latest approaches.

Sleep medicine reviews
Clinical sleep scoring involves a tedious visual review of overnight polysomnograms by a human expert, according to official standards. It could appear then a suitable task for modern artificial intelligence algorithms. Indeed, machine learning algor...

Hyper-G: An Artificial Intelligence Tool for Optimal Decision-Making and Management of Blood Glucose Levels in Surgery Patients.

Methods of information in medicine
BACKGROUND: Hyperglycemia or high blood glucose during surgery is associated with poor postoperative outcome. Knowing in advance which patients may develop hyperglycemia allows optimal assignment of resources and earlier initiation of glucose managem...

Massive metagenomic data analysis using abundance-based machine learning.

Biology direct
BACKGROUND: Metagenomics is the application of modern genomic techniques to investigate the members of a microbial community directly in their natural environments and is widely used in many studies to survey the communities of microbial organisms th...

Maximal flexibility in dynamic functional connectivity with critical dynamics revealed by fMRI data analysis and brain network modelling.

Journal of neural engineering
OBJECTIVE: The exploration of time-varying functional connectivity (FC) through human neuroimaging techniques provides important new insights on the spatio-temporal organization of functional communication in the brain's networks and its alterations ...

Integrating biomedical research and electronic health records to create knowledge-based biologically meaningful machine-readable embeddings.

Nature communications
In order to advance precision medicine, detailed clinical features ought to be described in a way that leverages current knowledge. Although data collected from biomedical research is expanding at an almost exponential rate, our ability to transform ...

Multiscale Road Extraction in Remote Sensing Images.

Computational intelligence and neuroscience
Recent advances in convolutional neural networks (CNNs) have shown impressive results in semantic segmentation. Among the successful CNN-based methods, U-Net has achieved exciting performance. In this paper, we proposed a novel network architecture b...

Ontology-based metabolomics data integration with quality control.

Bioanalysis
 The complications that arise when performing meta-analysis of datasets from multiple metabolomics studies are addressed with computational methods that ensure data quality, completeness of metadata and accurate interpretation across studies. This p...

An Ontology to Standardize Research Output of Nutritional Epidemiology: From Paper-Based Standards to Linked Content.

Nutrients
BACKGROUND: The use of linked data in the Semantic Web is a promising approach to add value to nutrition research. An ontology, which defines the logical relationships between well-defined taxonomic terms, enables linking and harmonizing research out...