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HDN-DDI: a novel framework for predicting drug-drug interactions using hierarchical molecular graphs and enhanced dual-view representation learning.
BMC Bioinformatics
January 2025
School of Computer Science and Technology, University of Science and Technology of China, 443 Huangshan Road, Hefei, 230027, China.
Background: Drug-drug interactions (DDIs) especially antagonistic ones present significant risks to patient safety, underscoring the urgent need for reliable prediction methods. Recently, substructure-based DDI prediction has garnered much attention due to the dominant influence of functional groups and substructures on drug properties. However, existing approaches face challenges regarding the insufficient interpretability of identified substructures and the isolation of chemical substructures.
View Article and Find Full Text PDFPredicting early diagnosis of intensive care unit-acquired weakness in septic patients using critical ultrasound and biological markers.
BMC Anesthesiol
January 2025
Department of Critical Care Medicine, West China Hospital, Sichuan University, 37 Guo Xue Xiang St, Chengdu, 610041, Sichuan, China.
Objective: Early diagnosis of intensive care unit-acquired weakness (ICUAW) is crucial for improving the outcomes of critically ill patients. Hence, this study was designed to identify predisposing factors for ICUAW and establish a predictive model for the early diagnosis of ICUAW.
Methods: This prospective observational multicenter study included septic patients from the comprehensive ICUs of West China Hospital of Sichuan University and 10 other hospitals between September and November 2023.
Implementation of An Advance Care Planning Intervention in Dialysis Clinics.
Am J Kidney Dis
January 2025
School of Nursing, University of Wisconsin-Madison, Madison, WI, U.S.A.
Rationale & Objective: Sharing Patient's Illness Representations to Increase Trust (SPIRIT) is an evidence-based advance care planning intervention targeting dialysis patients and their surrogate decision-makers. To address SPIRIT's implementation potential, we report on a process evaluation in our recently completed five-state cluster-randomized trial.
Study Design: A descriptive study of implementation within a randomized clinical trial.
ARCH: Large-scale knowledge graph via aggregated narrative codified health records analysis.
J Biomed Inform
January 2025
Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, 02115, MA, USA; VA Boston Healthcare System, 150 S Huntington Ave, Boston, 02130, MA, USA. Electronic address:
Objective: Electronic health record (EHR) systems contain a wealth of clinical data stored as both codified data and free-text narrative notes (NLP). The complexity of EHR presents challenges in feature representation, information extraction, and uncertainty quantification. To address these challenges, we proposed an efficient Aggregated naRrative Codified Health (ARCH) records analysis to generate a large-scale knowledge graph (KG) for a comprehensive set of EHR codified and narrative features.
View Article and Find Full Text PDFSeverity grading of hypertensive retinopathy using hybrid deep learning architecture.
Comput Methods Programs Biomed
January 2025
Regional Institute of Ophthalmology, Indira Gandhi Institute of Medical Sciences, Patna, 800025, Bihar, India.
Background And Objectives: Hypertensive Retinopathy (HR) is a retinal manifestation resulting from persistently elevated blood pressure. Severity grading of HR is essential for patient risk stratification, effective management, progression monitoring, timely intervention, and minimizing the risk of vision impairment. Computer-aided diagnosis and artificial intelligence (AI) systems play vital roles in the diagnosis and grading of HR.
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