AIMC Topic: Machine Learning

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Microbiome composition and implications for ballast water classification using machine learning.

The Science of the total environment
Ballast water is a vector for global translocation of microorganisms, and should be monitored to protect human and environmental health. This study utilizes high throughput sequencing (HTS) and machine learning to examine the bacterial and fungal mic...

Identifying depression in the National Health and Nutrition Examination Survey data using a deep learning algorithm.

Journal of affective disorders
BACKGROUND: As depression is the leading cause of disability worldwide, large-scale surveys have been conducted to establish the occurrence and risk factors of depression. However, accurately estimating epidemiological factors leading up to depressio...

Machine learning-based coronary artery disease diagnosis: A comprehensive review.

Computers in biology and medicine
Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) and often leads to a heart attack. It annually causes millions of deaths and billions of dollars in financial losses worldwide. Angiography, which is invasive and risky, is...

Computer Vision Classification of Barley Flour Based on Spatial Pyramid Partition Ensemble.

Sensors (Basel, Switzerland)
Imaging sensors are largely employed in the food processing industry for quality control. Flour from malting barley varieties is a valuable ingredient in the food industry, but its use is restricted due to quality aspects such as color variations and...

Discovery of food identity markers by metabolomics and machine learning technology.

Scientific reports
Verification of food authenticity establishes consumer trust in food ingredients and components of processed food. Next to genetic or protein markers, chemicals are unique identifiers of food components. Non-targeted metabolomics is ideally suited to...

Development and validation of three machine-learning models for predicting multiple organ failure in moderately severe and severe acute pancreatitis.

BMC gastroenterology
BACKGROUND: Multiple organ failure (MOF) is a serious complication of moderately severe (MASP) and severe acute pancreatitis (SAP). This study aimed to develop and assess three machine-learning models to predict MOF.

Deep multiphysics: Coupling discrete multiphysics with machine learning to attain self-learning in-silico models replicating human physiology.

Artificial intelligence in medicine
OBJECTIVES: The objective of this study is to devise a modelling strategy for attaining in-silico models replicating human physiology and, in particular, the activity of the autonomic nervous system.

Efficiently searching through large tACS parameter spaces using closed-loop Bayesian optimization.

Brain stimulation
BACKGROUND: Selecting optimal stimulation parameters from numerous possibilities is a major obstacle for assessing the efficacy of non-invasive brain stimulation.

SuperOrder: Provider order recommendation system for outpatient clinics.

Health informatics journal
This study aims at developing SuperOrder, an order recommendation system for outpatient clinics. Using the electronic health record data available at midnight, SuperOrder predicts the order contents for each upcoming appointment on a daily basis. A t...

Learning-based classification of valence emotion from electroencephalography.

The International journal of neuroscience
The neuroimaging research field has been revolutionized with the development of human cognitive functions without the use of brain pathways. To assist such systems, electroencephalography (EEG) based measures play an important role. In this study, th...