AIMC Topic: Machine Learning

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Alternative data mining/machine learning methods for the analytical evaluation of food quality and authenticity - A review.

Food research international (Ottawa, Ont.)
In recent years, the variety and volume of data acquired by modern analytical instruments in order to conduct a better authentication of food has dramatically increased. Several pattern recognition tools have been developed to deal with the large vol...

Using automated computer vision and machine learning to code facial expressions of affect and arousal: Implications for emotion dysregulation research.

Development and psychopathology
As early as infancy, caregivers' facial expressions shape children's behaviors, help them regulate their emotions, and encourage or dissuade their interpersonal agency. In childhood and adolescence, proficiencies in producing and decoding facial expr...

iMotor-CNN: Identifying molecular functions of cytoskeleton motor proteins using 2D convolutional neural network via Chou's 5-step rule.

Analytical biochemistry
Motor proteins are the driving force behind muscle contraction and are responsible for the active transportation of most proteins and vesicles in the cytoplasm. There are three superfamilies of cytoskeletal motor proteins with various molecular funct...

Machine Learning and Other Emerging Decision Support Tools.

Clinics in laboratory medicine
Emerging applications of machine learning and artificial intelligence offer the opportunity to discover new clinical knowledge through secondary exploration of existing patient medical records. This new knowledge may in turn offer a foundation to bui...

Feature-weighted survival learning machine for COPD failure prediction.

Artificial intelligence in medicine
Chronic obstructive pulmonary disease (COPD) yields a high rate of failures such as hospital readmission and death in the United States, Canada and worldwide. COPD failure imposes a significant social and economic burden on society, and predicting su...

Dissecting the Catecholamines: How New Approaches Will Facilitate the Distinction between Noradrenergic and Dopaminergic Systems.

ACS chemical neuroscience
To overcome the limitations of fast scan cyclic voltammetry (FSCV) to discriminate between catecholamines, we discuss new approaches to monitor the dynamics of these neurochemicals with high spatial, genetic, and temporal specificity.

Machine learning for patient risk stratification for acute respiratory distress syndrome.

PloS one
BACKGROUND: Existing prediction models for acute respiratory distress syndrome (ARDS) require manual chart abstraction and have only fair performance-limiting their suitability for driving clinical interventions. We sought to develop a machine learni...

Multisource Transfer Learning for Cross-Subject EEG Emotion Recognition.

IEEE transactions on cybernetics
Electroencephalogram (EEG) has been widely used in emotion recognition due to its high temporal resolution and reliability. Since the individual differences of EEG are large, the emotion recognition models could not be shared across persons, and we n...

Development of a machine learning algorithm predicting discharge placement after surgery for spondylolisthesis.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
PURPOSE: We aimed to develop a machine learning algorithm that can accurately predict discharge placement in patients undergoing elective surgery for degenerative spondylolisthesis.

Comparison between two programs for image analysis, machine learning and subsequent classification.

Tissue & cell
In the early 1950s, flow cytometry was developed as the first method for automated quantitative cellular analysis. In the early 1990s, the first equipment for image cytometry (laser scanning cytometry, LSC) became commercially available. As flow cyto...