Latest AI and machine learning research in practice management for healthcare professionals.
Research on long non-coding RNAs (lncRNAs) has garnered significant attention due to their critical roles in gene regulation and disease mechanisms. However, the complexity and diversity of lncRNA sequences, along with the limited knowledge of their functional mechanisms and the regulation of their expressions, pose significant challenges to lncRNA studies. Given the tremendous success of large ...
We propose a novel approach to effectively detect cloned identities of social-sensor cloud service providers (i.e. social media users) in the face of incomplete non-privacy-sensitive profile data. Named ICD-IPD, the proposed approach first extracts account pairs with similar usernames or screen names from a given set of user accounts collected from a social media. It then learns a multi-view rep...
An adversarial example is a modified input image designed to cause a Machine Learning (ML) model to make a mistake; these perturbations are often in...
\textbf{Trial design} Crossover randomized controlled trial. \textbf{Methods} An AI tool, Easy-ICD, was developed to assist clinical coders and was ...
Veterinary medical records represent a large data resource for application to veterinary and One Health clinical research efforts. Use of the data i...
Large Language Models (LLMs) have demonstrated remarkable performance across various domains, including healthcare. However, their ability to effect...
Bayesian models of cognition have gained considerable traction in computational neuroscience and psychiatry. Their scopes are now expected to expand...
Spiking neural network (SNN) has emerged as a promising paradigm in computational neuroscience and artificial intelligence, offering advantages such...
Medication Extraction and Mining play an important role in healthcare NLP research due to its practical applications in hospital settings, such as t...
Encapsulating a patient's clinical narrative into a condensed, informative summary is indispensable to clinical coding. The intricate nature of the cl...
Medical coding is essential for standardizing clinical data and communication but is often time-consuming and prone to errors. Traditional Natural L...
Abdominal computed tomography (CT) scans are frequently performed in clinical settings. Opportunistic CT involves repurposing routine CT images to e...
This paper aims to demonstrate how the prevalent practice in the learned video compression community of averaging rate-distortion (RD) curves across...
Fungal simulation and control are considered crucial techniques in Bio-Art creation. However, coding algorithms for reliable fungal simulations have...
Gene and RNA editing methods, technologies, and applications are emerging as innovative forms of therapy and medicine, offering more efficient imple...
Large language models (LLMs) have shown potential in biomedical applications, leading to efforts to fine-tune them on domain-specific data. However,...
Given the conference's focus on innovative healthcare solutions, our investigation into robotic assistance systems highlights crucial advancements in ...
Annotated language resources derived from clinical routine documentation form an intriguing asset for secondary use case scenarios. In this investigat...
Ontologies play a key role in representing and structuring domain knowledge. In the biomedical domain, the need for this type of representation is cru...
Coding according to the International Classification of Diseases (ICD)-10 and its clinical modifications (CM) is inherently complex and expensive. Nat...