Artificial Intelligence Medical Compendium

Regularization methods using prior knowledge are essential in solving ill-posed inverse problems such as Electrical Impedance Tomography (EIT). However, designing effective regularization and integrating prior information into EIT remains challenging... read more 

Continuous chromatographic processes involve nonlinear dynamics, cyclic operation, and strong interactions between operating variables, which make real-time control inherently complex. As process variability and integration increase, there is a growi... read more 

Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry (MALDI-MS) is a powerful tool for profiling complex biological samples. However, large-scale metabolomics experiments often suffer from substantial batch effects caused by variations in sa... read more 

Graph Neural Networks (GNNs) have demonstrated remarkable success, yet they often exhibit limitations in capturing the complex local heterogeneity inherent in real-world graphs. While the Mixture of Experts (MoE) paradigm offers a promising direction... read more 

BACKGROUND: The digitisation of healthcare has generated vast amounts of data in various formats, including free-text notes, tabular records and medical images. This data is critical for research and innovation, but often contains sensitive informati... read more 

Traditional deep learning approaches in medical image analysis usually focus on either segmentation or classification, which limits their ability to exploit shared information between these interrelated tasks. Recent multi-task learning (MTL) methods... read more 

BACKGROUND: There is increasing global concern about the harms associated with problematic usage of the internet (PUI) affecting young people. Various risk factors have been proposed, but there is a scarcity of reliable evidence on the extent of the ... read more 

Aquatic deoxygenation requires efficient and durable oxygenation strategies. Here, we developed a data-efficient framework that integrates regression analysis, machine learning, and particle swarm optimization to guide the inverse design of biochar-b... read more 

Climate change is altering hydrology, land cover, and biogeochemistry in Alpine river systems, yet predictive understanding of dissolved organic carbon (DOC) and total suspended solids (TSS), across glacierised and lowland catchments remains limited.... read more 

BACKGROUND AND OBJECTIVE: Multivariate physiological time series classification is essential for healthcare decision support within the Internet of Medical Things (IoMT). However, existing methods often struggle with high noise, non-stationarity, and... read more