AIMC Topic: Machine Learning

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Paradigm Shift Toward Digital Neuropsychology and High-Dimensional Neuropsychological Assessments: Review.

Journal of medical Internet research
Neuropsychologists in the digital age have increasing access to emerging technologies. The National Institutes of Health (NIH) initiatives for behavioral and social sciences have emphasized these developing scientific and technological potentials (eg...

Artificial Intelligence in Coronary Computed Tomography Angiography: From Anatomy to Prognosis.

BioMed research international
Cardiac computed tomography angiography (CCTA) is widely used as a diagnostic tool for evaluation of coronary artery disease (CAD). Despite the excellent capability to rule-out CAD, CCTA may overestimate the degree of stenosis; furthermore, CCTA anal...

Predicting the DNA Conductance Using a Deep Feedforward Neural Network Model.

Journal of chemical information and modeling
Double-stranded DNA (dsDNA) has been established as an efficient medium for charge migration, bringing it to the forefront of the field of molecular electronics and biological research. The charge migration rate is controlled by the electronic coupli...

Domain specific word embeddings for natural language processing in radiology.

Journal of biomedical informatics
BACKGROUND: There has been increasing interest in machine learning based natural language processing (NLP) methods in radiology; however, models have often used word embeddings trained on general web corpora due to lack of a radiology-specific corpus...

Machine Learning for Child and Adolescent Health: A Systematic Review.

Pediatrics
CONTEXT: In the last few decades, data acquisition and processing has seen tremendous amount of growth, thus sparking interest in machine learning (ML) within the health care system.

Learning fine-grained estimation of physiological states from coarse-grained labels by distribution restoration.

Scientific reports
Due to its importance in clinical science, the estimation of physiological states (e.g., the severity of pathological tremor) has aroused growing interest in machine learning community. While the physiological state is a continuous variable, its cont...

Imbalanced learning: Improving classification of diabetic neuropathy from magnetic resonance imaging.

PloS one
One of the fundamental challenges when dealing with medical imaging datasets is class imbalance. Class imbalance happens where an instance in the class of interest is relatively low, when compared to the rest of the data. This study aims to apply ove...

Optimised genetic algorithm-extreme learning machine approach for automatic COVID-19 detection.

PloS one
The coronavirus disease (COVID-19), is an ongoing global pandemic caused by severe acute respiratory syndrome. Chest Computed Tomography (CT) is an effective method for detecting lung illnesses, including COVID-19. However, the CT scan is expensive a...