BACKGROUND: The atherogenic index of plasma (AIP) is considered an important marker of atherosclerosis and cardiovascular risk. However, its potential role in predicting length of stay (LOS), especially in patients with atherosclerotic cardiovascular...
OBJECTIVE: To determine whether readily available patient, ultrasound and treatment outcome data can be used to develop, validate and externally test two machine learning (ML) models for predicting the success of expectant and medical management of m...
This work presents a novel Voice in Head (ViH) framework, that integrates Large Language Models (LLMs) and the power of semantic understanding to enhance robotic navigation and interaction within complex environments. Our system strategically combine...
Accurate molecular property prediction is crucial for drug discovery and computational chemistry, facilitating the identification of promising compounds and accelerating therapeutic development. Traditional machine learning falters with high-dimensio...
Automatically detecting sound events with Artificial Intelligence (AI) has become increas- ingly popular in the field of bioacoustics, ecoacoustics, and soundscape ecology, particularly for wildlife monitoring and conservation. Conventional methods p...
INTRODUCTION: Valganciclovir, a prodrug of ganciclovir (GCV), is used to prevent cytomegalovirus infection after transplantation, with doses adjusted based on creatinine clearance (CrCL) to target GCV AUC0-24 h of 40-60 mg*h/L. This sometimes leads t...
The Animal-AI Environment is a unique game-based research platform designed to facilitate collaboration between the artificial intelligence and comparative cognition research communities. In this paper, we present the latest version of the Animal-AI ...
This study proposes a method for generating complex and long-horizon off-line task plans using large language models (LLMs). Although several studies have been conducted in recent years on robot task planning using LLMs, the planning results tend to ...
This study introduces a novel deep learning approach aimed at accurately classifying Gallbladder Cancer (GBC) into benign, malignant, and normal categories using ultrasound images from the challenging GBC USG (GBCU) dataset. The proposed methodology ...
With the explosive increase in genome sequence data, perhaps the major challenge in natural-product-based drug discovery is the identification of gene clusters most likely to specify new chemistry and bioactivities. We discuss the challenges and stat...
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