AIMC Topic: Algorithms

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A deep drug prediction framework for viral infectious diseases using an optimizer-based ensemble of convolutional neural network: COVID-19 as a case study.

Molecular diversity
The SARS-CoV-2 outbreak highlights the persistent vulnerability of humanity to epidemics and emerging microbial threats, emphasizing the lack of time to develop disease-specific treatments. Therefore, it appears beneficial to utilize existing resourc...

Implementation of an AI Algorithm in Clinical Practice to Reduce Missed Incidental Pulmonary Embolisms on Chest CT and Its Impact on Short-Term Survival.

Investigative radiology
OBJECTIVES: A substantial number of incidental pulmonary embolisms (iPEs) in computed tomography scans are missed by radiologists in their daily routine. This study analyzes the radiological reports of iPE cases before and after implementation of an ...

A Molecular Fragment Representation Learning Framework for Drug-Drug Interaction Prediction.

Interdisciplinary sciences, computational life sciences
The concurrent use of multiple drugs may result in drug-drug interactions, increasing the risk of adverse reactions. Hence, it is particularly crucial to propose computational methods for precisely identifying unknown drug-drug interactions, which is...

Support matrix machine: A review.

Neural networks : the official journal of the International Neural Network Society
Support vector machine (SVM) is one of the most studied paradigms in the realm of machine learning for classification and regression problems. It relies on vectorized input data. However, a significant portion of the real-world data exists in matrix ...

Context-aware feature reconstruction for class-incremental anomaly detection and localization.

Neural networks : the official journal of the International Neural Network Society
With the development of deep learning, the unsupervised visual anomaly detection and localization task has gained significant attention in both academia and industry, where only normal data are used for training. Existing methods for this task typica...

Modelling multivariate spatio-temporal data with identifiable variational autoencoders.

Neural networks : the official journal of the International Neural Network Society
Modelling multivariate spatio-temporal data with complex dependency structures is a challenging task but can be simplified by assuming that the original variables are generated from independent latent components. If these components are found, they c...

Federated learning and deep learning framework for MRI image and speech signal-based multi-modal depression detection.

Computational biology and chemistry
Adolescence is a significant period for developing skills and knowledge and learning about managing relationships and emotions by gathering attributes for maturity. Recently, Depression arises as a common mental health issue in adolescents and this a...

Bootstrap each lead's latent: A novel method for self-supervised learning of multilead electrocardiograms.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Electrocardiogram (ECG) is one of the most important diagnostic tools for cardiovascular diseases (CVDs). Recent studies show that deep learning models can be trained using labeled ECGs to achieve automatic detection of CVDs...

Advancements in maize disease detection: A comprehensive review of convolutional neural networks.

Computers in biology and medicine
This review article provides a comprehensive examination of the state-of-the-art in maize disease detection leveraging Convolutional Neural Networks (CNNs). Beginning with the intrinsic significance of plants and the pivotal role of maize in global a...

Serum Potassium Monitoring UsingĀ AI-Enabled Smartwatch Electrocardiograms.

JACC. Clinical electrophysiology
BACKGROUND: Hyperkalemia, characterized by elevated serum potassium levels, heightens the risk of sudden cardiac death, particularly increasing risk for individuals with chronic kidney disease and end-stage renal disease (ESRD). Traditional laborator...