Latest AI and machine learning research in critical care for healthcare professionals.
BackgroundWith the advancement of Artificial Intelligence (AI), clinical engineering has witnessed t...
Recent advances in artificial intelligence-based audio and speech processing have increasingly focus...
Purpose To assess the prognostic value of an open-source deep learning-based chest radiographs algor...
To evaluate the accuracy of a Bidirectional Encoder Representations for Transformers (BERT) Natural...
We created and validated an open-access AI algorithm (AIc) for assessing image segmentation and pati...
BACKGROUND: Sepsis, a complex inflammatory condition with high mortality rates, lacks effective trea...
Advances in wearable sensors and artificial intelligence have greatly enhanced the potential of digi...
BACKGROUND: Artificial intelligence (AI) is poised to transform point-of-care practice by providing ...
OBJECTIVE: Prediction of mortality in intensive care unit (ICU) patients typically relies on black b...
MOTIVATION: Spatial transcriptomics (ST) addresses the loss of spatial context in single-cell RNA-se...
Septic acute respiratory distress syndrome (ARDS) is a complex and noteworthy type, but its molecula...
The accurate categorization of compounds within the anatomical therapeutic chemical (ATC) system is ...
Multi-omics data often suffer from the "big $p$, small $n$" problem where the dimensionality of feat...
Identifying spatial domains for spatial transcriptomics is crucial for achieving comprehensive insig...
Single-cell multi-omics technologies have revolutionized the study of cell states and functions by s...
Drug response prediction (DRP) methods tackle the complex task of associating the effectiveness of s...
The rapid advancement of next-generation sequencing (NGS) technology and the expanding availability ...
Accurate cancer survival prediction remains a critical challenge in clinical oncology, largely due t...
The complementary information found in different modalities of patient data can aid in more accurate...
Within a recent decade, graph neural network (GNN) has emerged as a powerful neural architecture for...
Background Recent studies have investigated how deep learning (DL) algorithms applied to CT using tw...