Latest AI and machine learning research in prescriptions for healthcare professionals.
ABSTRACT Objectives: To determine whether heterogeneous treatment effects (HTE) explain the inconclusive results of targeted temperature management (TTM) trials after cardiac arrest, using causal machine learning across four datasets. Design: Secondary analysis of one multicenter RCT and three observational ICU cohorts using S-learner and forest-based R-learner models to estimate conditional avera...
Most clinical trials fail due to either lack of efficacy or safety concerns. Human genetics can address both failure reasons: disease-associated genes are not only promising therapeutic targets but also predict drug side effects. However, because the same genetic signal underlies both outcomes, we need methods that disentangle which disease genes mediate therapeutic benefit versus adverse side eff...
Predicting drug-target interactions (DTIs) with deep learning offers opportunities to accelerate drug discovery, yet performance is constrained by the...
Rural thematic road network construction aims to extract topological road structures from movement trajectory images of agricultural machinery. Howeve...
High-throughput accurate protein-protein interaction (PPI) prediction is foundational to systems-level biological understanding, disease mechanism dis...
Mechanistically predicting the consequences of drug action requires distinguishing whether molecular interactions are activating or inhibitory, yet mo...
Clinical time-series forecasting is increasingly studied for decision support, yet standard aggregate metrics can obscure whether a model is actually ...
Systematic Reviews (SRs) are the gold standard for evidence synthesis, but the manual title and abstract screening of thousands of references creates ...
Protein post-translational modifications (PTMs), particularly phosphorylation, serve as the primary molecular switches that orchestrate cellular signa...
OpenAI's GPT-Image-2 has effectively erased the visual boundary between authentic and AI-edited document images: a single number on a receipt can be r...
In autonomous driving, camera-radar fusion offers complementary sensing and low deployment cost. Existing methods perform fusion through input mixing,...
Touchless interaction with medical images is becoming increasingly important in the surgical field, where sterility and continuity of the operational ...
Progression to dialysis or end-stage renal disease is a rare but clinically important outcome. Clinicians need evidence on how medication exposures in...
Disease progression varies with age and is influenced by underlying genetic, biochemical, and hormonal etiologies, suggesting the need for tailored mo...
Clinical risk prediction using longitudinal medical data supports individualized care. Self-supervised foundation models have emerged as a promising a...
Graph Transformers can mix information globally, but this flexibility also creates failure modes: some tasks require long-range communication while ot...
Drugs induce coordinated phenotypic changes across multiple modalities, including transcriptional reprogramming and cellular morphological remodeling....
Despite decades of progress in computational drug discovery, deep learning-based molecular representation models remain largely structure-centric, ass...
Background: Chronic conditions such as hypertension can significantly disrupt daily life and emotional wellbeing. The interaction between patients' pe...
Background Large language models (LLMs) are increasingly used in medication-related tasks despite limited evidence supporting their accuracy and safet...