AIMC Topic: Machine Learning

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Generating post-hoc explanations for Skip-gram-based node embeddings by identifying important nodes with bridgeness.

Neural networks : the official journal of the International Neural Network Society
Node representation learning in a network is an important machine learning technique for encoding relational information in a continuous vector space while preserving the inherent properties and structures of the network. Recently, unsupervised node ...

Prediction of disease comorbidity using explainable artificial intelligence and machine learning techniques: A systematic review.

International journal of medical informatics
OBJECTIVE: Disease comorbidity is a major challenge in healthcare affecting the patient's quality of life and costs. AI-based prediction of comorbidities can overcome this issue by improving precision medicine and providing holistic care. The objecti...

Multiclass classification of environmental chemical stimuli from unbalanced plant electrophysiological data.

PloS one
Plant electrophysiological response contains useful signature of its environment and health which can be utilized using suitable statistical analysis for developing an inverse model to classify the stimulus applied to the plant. In this paper, we hav...

Machine learning in cardiology: Clinical application and basic research.

Journal of cardiology
Machine learning is a subfield of artificial intelligence. The quality and versatility of machine learning have been rapidly improving and playing a critical role in many aspects of social life. This trend is also observed in the medical field. Gener...

Deep learning-based assessment of knee septic arthritis using transformer features in sonographic modalities.

Computer methods and programs in biomedicine
PURPOSE: Septic arthritis is an infectious disease. Conventionally, the diagnosis of septic arthritis can only be based on the identification of causal pathogens taken from synovial fluid, synovium or blood samples. However, the cultures require seve...

Learnable latent embeddings for joint behavioural and neural analysis.

Nature
Mapping behavioural actions to neural activity is a fundamental goal of neuroscience. As our ability to record large neural and behavioural data increases, there is growing interest in modelling neural dynamics during adaptive behaviours to probe neu...

A machine learning approach for the diagnosis of obstructive sleep apnoea using oximetry, demographic and anthropometric data.

Singapore medical journal
INTRODUCTION: Obstructive sleep apnoea (OSA) is a serious but underdiagnosed condition. Demand for the gold standard diagnostic polysomnogram (PSG) far exceeds its availability. More efficient diagnostic methods are needed, even in tertiary settings....

Prediction meets time series with gaps: User clusters with specific usage behavior patterns.

Artificial intelligence in medicine
With mHealth apps, data can be recorded in real life, which makes them useful, for example, as an accompanying tool in treatments. However, such datasets, especially those based on apps with usage on a voluntary basis, are often affected by fluctuati...

A machine learning analysis to evaluate the outcome measures in inflammatory myopathies.

Autoimmunity reviews
OBJECTIVE: To assess the long-term outcome in patients with Idiopathic Inflammatory Myopathies (IIM), focusing on damage and activity disease indexes using artificial intelligence (AI).

Evaluation of the dataset quality in gamma passing rate predictions using machine learning methods.

The British journal of radiology
OBJECTIVE: Gamma passing rate (GPR) predictions using machine learning methods have been explored for treatment verification of radiotherapy plans. However, these methods presented datasets with unbalanced number of plans having different treatment c...