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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.
Revision of institutional accreditation standards and processes in Iranian medical universities.
J Educ Health Promot
November 2024
Medical Education Research Center, Department of Medical Education, Isfahan University of Medical Sciences, Isfahan, Iran.
Background: The institutional accreditation standards were revised after the initial round of institutional accreditation and gaining experience while scrutinizing issues and complications. This research covers the steps needed to update and then compile the new standards, making the necessary changes in regulations, self-evaluation guides, external evaluation, ethics, and the required forms.
Materials And Methods: This comprehensive developmental study, conducted in two main phases from 2019 to 2022 at Isfahan University of Medical Sciences, employed a variety of rigorous methods.
Evidence summary on management strategies for gastroesophageal reflux symptoms in patients following esophageal cancer surgery.
Asia Pac J Oncol Nurs
December 2025
Esophageal Oncology Department, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.
Objective: This study aimed to summarize evidence-based strategies for the self-management of gastroesophageal reflux symptoms (GERS) at home among patients who have undergone esophageal cancer surgery, providing practical references for clinical practice.
Methods: A systematic evidence summary was conducted based on the reporting standards of the Fudan University Center for Evidence-based Nursing. Literature was retrieved from international and Chinese databases, including guidelines, expert consensus, systematic reviews, and original studies.
Robust RNA secondary structure prediction with a mixture of deep learning and physics-based experts.
Biol Methods Protoc
January 2025
Department of Physics, George Washington University, Washington, DC 20052, United States.
A mixture-of-experts (MoE) approach has been developed to mitigate the poor out-of-distribution (OOD) generalization of deep learning (DL) models for single-sequence-based prediction of RNA secondary structure. The main idea behind this approach is to use DL models for in-distribution (ID) test sequences to leverage their superior ID performances, while relying on physics-based models for OOD sequences to ensure robust predictions. One key ingredient of the pipeline, named MoEFold2D, is automated ID/OOD detection via consensus analysis of an ensemble of DL model predictions without requiring access to training data during inference.
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.
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