Latest AI and machine learning research in prescriptions for healthcare professionals.
Background: Conventional evaluations of digital health interventions typically assess mean treatment effects, potentially masking heterogeneous impacts across the functional recovery distribution. Patients at the lower and upper tails of recovery trajectories may respond differently to AI-enhanced telerehabilitation, yet standard regression approaches cannot capture these distributional nuances. O...
Background: Traditional pharmacovigilance methods based on biostatistical approaches systematically exclude outliers and rare events, potentially missing critical safety signals. These methods fail to detect micro-clusters of adverse events and comorbidity patterns that may indicate serious but low frequency adverse drug reactions (ADRs). We introduce the concept of 'absurdity signal detection' of...
High-throughput drug combination screens motivate computational methods to identify synergistic pairs, yet synergy is typically quantified by heuristi...
Randomized controlled trials estimate average treatment effects, but treatment response heterogeneity motivates personalized approaches. A critical qu...
Human perception for effective object tracking in a 2D video stream arises from the implicit use of prior 3D knowledge combined with semantic reasonin...
Recent advancements in image generation models have enabled the prediction of future Graphical User Interface (GUI) states based on user instructions....
Drug discovery remains time-consuming, labor-intensive, and expensive, often requiring years and substantial investment per drug candidate. Predicting...
Video motion transfer aims to synthesize videos by generating visual content according to a text prompt while transferring the motion pattern observed...
In pharmacovigilance, analyzing drug safety cases is often time consuming due to the abundance of laboratory data, complex medical histories, and intr...
Background and Objective: Blood pressure treatment response is variable in individual patients, and the choice of medical therapy is often dependent o...
Computational screening is increasingly becoming a crucial aspect of Antibody Drug Conjugate (ADC) research, allowing the elimination of dead ends at ...
Neural representations rely on the ability of neuronal assemblies to display organized spiking patterns, despite being embedded within noisy networks....
Dialogical actions are contingent in humans and also need to be contingent when implemented on intelligent systems such as social robots in order to e...
What are the mechanisms that enable organisms to detect and respond to the actions of others? Social contingency, or the degree to which one's actions...
The emergence of Janus kinase (JAK) inhibitors, a relatively new class of medications for autoimmune and inflammatory conditions, has been accompanied...
Visual Place Recognition (VPR) is a key component for localisation in GNSS-denied environments, but its performance critically depends on selecting an...
Large-scale pharmacogenomic screens provide extensive measurements of drug response across diverse cancer cell lines; however, most computational appr...
Camera-based visible light positioning (VLP) is a promising technique for accurate and low-cost indoor camera pose estimation (CPE). To reduce the num...
Composed Image Retrieval (CIR) aims to retrieve target images based on a hybrid query comprising a reference image and a modification text. Early dual...
Image-based object removal often erases only the named target, leaving behind interaction evidence that renders the result semantically inconsistent. ...