98%
921
2 minutes
20
Objectives: To select, present, and summarize cutting edge work in the field of Knowledge Representation and Management (KRM) published in 2022 and 2023.
Methods: A comprehensive set of KRM-relevant articles published in 2022 and 2023 was retrieved by querying PubMed. Topic modeling with Latent Dirichlet Allocation was used to further refine this query and suggest areas of focus. Selected articles were chosen based on a review of their title and abstract.
Results: An initial set of 8,706 publications were retrieved from PubMed. From these, fifteen papers were ultimately selected matching one of two main themes: KRM for long COVID, and KRM approaches used in combination with generative large language models.
Conclusions: This survey shows the ongoing development and versatility of KRM approaches, both to improve our understanding of a global health crisis and to augment and evaluate cutting edge technologies from other areas of artificial intelligence.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12020515 | PMC |
http://dx.doi.org/10.1055/s-0044-1800747 | DOI Listing |
J Glaucoma
September 2025
Harvard Medical School, Boston, MA.
Purpose: Large language models (LLMs) can assist patients who seek medical knowledge online to guide their own glaucoma care. Understanding the differences in LLM performance on glaucoma-related questions can inform patients about the best resources to obtain relevant information.
Methods: This cross-sectional study evaluated the accuracy, comprehensiveness, quality, and readability of LLM-generated responses to glaucoma inquiries.
Trauma Surg Acute Care Open
September 2025
Medical Center of the Rockies, Loveland, Colorado, USA.
Introduction: Efforts to strengthen healthcare systems have led to the development of clinical practice guidance, defined as clinical decision-making aids built on scientific evidence, experiential knowledge, and ideally, patient values. This review evaluates the accessibility, relevance, and quality of existing trauma guidance globally.
Methods: A systematic review evaluated trauma-related clinical guidance sources published from 2016 to 2023, searching in English across eight databases and 28 professional society websites.
Health Inf Sci Syst
December 2025
Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000 China.
Leveraging natural language processing to identify anxiety states from social media has been widely studied. However, existing research lacks deep user-level semantic modeling and effective anxiety feature extraction. Additionally, the absence of clinical domain knowledge in current models limits their interpretability and medical relevance.
View Article and Find Full Text PDFJ Neurosci
September 2025
Werner Reichardt Centre for Integrative Neuroscience, University of Tübingen, 72076 Tübingen, Germany.
Despite the functional specialization in visual cortex, there is growing evidence that the processing of chromatic and spatial visual features is intertwined. While past studies focused on visual field biases in retina and behavior, large-scale dependencies between coding of color and retinotopic space are largely unexplored in the cortex. Using a sample of male and female volunteers, we asked whether spatial color biases are shared across different human observers, and whether they are idiosyncratic for distinct areas.
View Article and Find Full Text PDFIEEE Trans Neural Netw Learn Syst
September 2025
Knowledge distillation (KD) aims to transfer knowledge from a large-scale teacher model to a lightweight one, significantly reducing computational and storage requirements. However, the inherent learning capacity gap between the teacher and student often hinders the sufficient transfer of knowledge, motivating numerous studies to address this challenge. Inspired by the progressive approximation principle in the Stone-Weierstrass theorem, we propose expandable residual approximation (ERA), a novel KD method that decomposes the approximation of residual knowledge into multiple steps, reducing the difficulty of mimicking the teacher's representation through a divide-and-conquer approach.
View Article and Find Full Text PDF