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Understanding Health-Related Discussions on Reddit: Development of a Topic Assignment Method and Exploratory Analysis.
JMIR Form Res
January 2025
Larner College of Medicine, University of Vermont, Burlington, VT, United States.
Background: Social media has become a widely used way for people to share opinions about health care and medical topics. Social media data can be leveraged to understand patient concerns and provide insight into why patients may turn to the internet instead of the health care system for health advice.
Objective: This study aimed to develop a method to investigate Reddit posts discussing health-related conditions.
Technological Adjuncts to Streamline Patient Recruitment, Informed Consent, and Data Management Processes in Clinical Research: Observational Study.
JMIR Form Res
January 2025
Northwestern Medicine, Chicago, IL, United States.
Background: Patient recruitment and data management are laborious, resource-intensive aspects of clinical research that often dictate whether the successful completion of studies is possible. Technological advances present opportunities for streamlining these processes, thus improving completion rates for clinical research studies.
Objective: This paper aims to demonstrate how technological adjuncts can enhance clinical research processes via automation and digital integration.
Exploring Virtual Reality and Exercise Simulator Interventions in Patients With Attention Deficit Hyperactivity Disorder: Comprehensive Literature Review.
JMIR Serious Games
January 2025
School of Computing, Engineering and Mathematical Sciences, Optus Chair Digital Health, La Trobe University, Melbourne, Australia.
Background: This review explores virtual reality (VR) and exercise simulator-based interventions for individuals with attention-deficit/hyperactivity disorder (ADHD). Past research indicates that both VR and simulator-based interventions enhance cognitive functions, such as executive function and memory, though their impacts on attention vary.
Objective: This study aimed to contribute to the ongoing scientific discourse on integrating technology-driven interventions into the management and evaluation of ADHD.
Urgency Prediction for Medical Laboratory Tests Through Optimal Sparse Decision Tree: Case Study With Echocardiograms.
JMIR AI
January 2025
Department of Information Systems and Business Analytics, Iowa State University, Ames, IA, United States.
Background: In the contemporary realm of health care, laboratory tests stand as cornerstone components, driving the advancement of precision medicine. These tests offer intricate insights into a variety of medical conditions, thereby facilitating diagnosis, prognosis, and treatments. However, the accessibility of certain tests is hindered by factors such as high costs, a shortage of specialized personnel, or geographic disparities, posing obstacles to achieving equitable health care.
View Article and Find Full Text PDFDiagnostic Decision-Making Variability Between Novice and Expert Optometrists for Glaucoma: Comparative Analysis to Inform AI System Design.
JMIR Med Inform
January 2025
Department of Systems Design Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON, Canada.
Background: While expert optometrists tend to rely on a deep understanding of the disease and intuitive pattern recognition, those with less experience may depend more on extensive data, comparisons, and external guidance. Understanding these variations is important for developing artificial intelligence (AI) systems that can effectively support optometrists with varying degrees of experience and minimize decision inconsistencies.
Objective: The main objective of this study is to identify and analyze the variations in diagnostic decision-making approaches between novice and expert optometrists.
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