Download full-text PDF |
Source |
|---|
Publication Analysis
Top Keywords
Similar Publications
Development and evaluation of a 4M taxonomy from nursing home staff text messages using a fine-tuned generative language model.
J Am Med Inform Assoc
January 2025
Sinclair School of Nursing, University of Missouri, Columbia, MO 65211, United States.
Objective: This study aimed to explore the utilization of a fine-tuned language model to extract expressions related to the Age-Friendly Health Systems 4M Framework (What Matters, Medication, Mentation, and Mobility) from nursing home worker text messages, deploy automated mapping of these expressions to a taxonomy, and explore the created expressions and relationships.
Materials And Methods: The dataset included 21 357 text messages from healthcare workers in 12 Missouri nursing homes. A sample of 860 messages was annotated by clinical experts to form a "Gold Standard" dataset.
Transformer-Based Tool for Automated Fact-Checking: A Pilot Study on Online Health Information.
JMIR Infodemiology
December 2024
Department of Management, Evaluation and Health Policy, School of Public Health, Université de Montréal, Montreal, CA.
Background: Many people seek health-related information online. The significance of reliable information became particularly evident due to the potential dangers of misinformation. Therefore, discerning true and reliable information from false information has become increasingly challenging.
View Article and Find Full Text PDFAutomated Detection of Filamentous Fungal Keratitis on Whole Slide Images of Potassium Hydroxide Smears with Multiple Instance Learning.
Ophthalmol Sci
November 2024
Casey Eye Institute, Oregon Health & Science University, Portland, Oregon.
Purpose: The diagnosis of fungal keratitis using potassium hydroxide (KOH) smears of corneal scrapings enables initiation of the correct antimicrobial therapy at the point-of-care but requires time-consuming manual examination and expertise. This study evaluates the efficacy of a deep learning framework, dual stream multiple instance learning (DSMIL), in automating the analysis of whole slide imaging (WSI) of KOH smears for rapid and accurate detection of fungal infections.
Design: Retrospective observational study.
Severity of metabolic derangement predicts survival after out-of-hospital cardiac arrest and the likelihood of benefiting from extracorporeal life support.
Emergencias
December 2024
Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seúl, República de Corea. Department of Digital Health, SAIHST, Sungkyunkwan University, Seúl, República de Corea.
Objective: To develop a Metabolic Derangement Score (MDS) based on parameters available after initial testing and assess the score's ability to predict survival after out-of hospital cardiac arrest (OHCA) and the likely usefulness of extracorporeal life support (ECLS).
Methods: A total of 5100 cases in the Korean Cardiac Arrest Research Consortium registry were included. Patients' mean age was 67 years, and 69% were men.
Characteristics and outcomes of out-of-hospital cardiac arrest among students under school supervision in Japan: a descriptive epidemiological study (2008-2021).
Environ Health Prev Med
January 2025
Division of Environmental Medicine and Population Sciences, Department of Social and Environmental Medicine, Graduate School of Medicine, Osaka University.
Background: A comprehensive understanding of the epidemiology of pediatric out-of-hospital cardiac arrest (OHCA) occurring under school supervision is lacking. We aimed to comprehensively describe the characteristics and outcomes of OHCA among students in elementary schools, junior high schools, high schools, and technical colleges in Japan.
Methods: OHCA data from 2008-2021 were obtained from the SPIRITS study, which provides a nationwide database of OHCAs occurring under school supervision across Japan.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!