Latest AI and machine learning research in product alert for healthcare professionals.
We investigated whether the plasma proteome distinguishes people with epilepsy who report central nervous system (CNS) side effects from antiseizure medications (ASMs) from those who do not. In 161 patients profiled using proximity extension assay-based proteomics Neurology and Inflammation panels (~1,447 proteins), we applied an ensemble leak-controlled machine-learning (ML) workflow based on LAS...
Pre-trained generative models for residential floor plans are typically optimized to fit large-scale data distributions, which can under-emphasize critical architectural priors such as the configurational dominance and connectivity of domestic public spaces (e.g., living rooms and foyers). This paper proposes Space Syntax-guided Post-training (SSPT), a post-training paradigm that explicitly inject...
Objective: The objective of this study is to develop a machine learning (ML)-based framework for early risk stratification of clinical trials (CTs) ac...
Background Accurate diagnostic tools are needed in schistosomiasis elimination settings to determine prevalence thresholds for assigning or stopping i...
Background: Biomedical Large Language Models (LLMs) combined with prompt engineering offer domain-specific reasoning, yet their application to individ...
Unified multimodal models can both understand and generate visual content within a single architecture. Existing models, however, remain data-hungry a...
The SARS-CoV-2 Delta variant (B.1.617.2), initially classified as a variant of concern due to its enhanced transmissibility and vaccine-escape mutatio...
Unified multimodal models can both understand and generate visual content within a single architecture. Existing models, however, remain data-hungry a...
Perinatal depression (PD) is common and disabling, yet its longitudinal comorbidity patterns and predictability remain poorly understood. This study l...
Post-training of flow matching models-aligning the output distribution with a high-quality target-is mathematically equivalent to imitation learning. ...
The absence of pre-hospital physiological data in standard clinical datasets fundamentally constrains the early prediction of stroke, as patients typi...
Deep learning has achieved expert-level performance in automated electrocardiogram (ECG) diagnosis, yet the "black-box" nature of these models hinders...
Background: Traditional pharmacovigilance methods based on biostatistical approaches systematically exclude outliers and rare events, potentially miss...
Medical document OCR is challenging due to complex layouts, domain-specific terminology, and noisy annotations, while requiring strict field-level exa...
Existing 3D Gaussian Splatting simplification methods commonly use importance scores, such as blending weights or sensitivity, to identify redundant G...
Safety alignment is only as robust as its weakest failure mode. Despite extensive work on safety post-training, it has been shown that models can be r...
In pharmacovigilance, analyzing drug safety cases is often time consuming due to the abundance of laboratory data, complex medical histories, and intr...
Aphasia, an acquired language deficit, is the most common post-stroke focal cognitive impairment, and roughly 60% cases become chronic (duration >6 mo...
The emergence of Janus kinase (JAK) inhibitors, a relatively new class of medications for autoimmune and inflammatory conditions, has been accompanied...
Large language models (LLMs) have achieved remarkable capabilities, yet methods to verify which model components are truly necessary for language func...