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| http://dx.doi.org/10.1177/00333549241275400 | DOI Listing |
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The feasibility of using machine learning to predict COVID-19 cases.
Int J Med Inform
January 2025
School of Geography and the Environment, University of Oxford, South Parks Road, Oxford OX1 3QY, United Kingdom. Electronic address:
Background: Coronavirus Disease 2019 (COVID-19), caused by the SARS-CoV-2 virus, emerged as a global health crisis in 2019, resulting in widespread morbidity and mortality. A persistent challenge during the pandemic has been the accuracy of reported epidemic data, particularly in underdeveloped regions with limited access to COVID-19 test kits and healthcare infrastructure. In the post-COVID era, this issue remains crucial.
View Article and Find Full Text PDFIdentification of an ANCA-associated vasculitis cohort using deep learning and electronic health records.
Int J Med Inform
January 2025
Rheumatology and Allergy Clinical Epidemiology Research Center and Division of Rheumatology, Allergy, and Immunology, and Mongan Institute, Department of Medicine, Massachusetts General Hospital Boston MA USA. Electronic address:
Background: ANCA-associated vasculitis (AAV) is a rare but serious disease. Traditional case-identification methods using claims data can be time-intensive and may miss important subgroups. We hypothesized that a deep learning model analyzing electronic health records (EHR) can more accurately identify AAV cases.
View Article and Find Full Text PDFCharacterizing Telehealth Barriers and Preferences to Promote Acceptable Implementation Strategies in Central Uganda: Multilevel Formative Evaluation.
JMIR Form Res
January 2025
Department of Epidemiology and Biostatistics, College of Human Medicine, Michigan State University, East Lansing, MI, United States.
Background: Telehealth approaches can address health care access barriers and improve care delivery in resource-limited settings around the globe. Yet, telehealth adoption in Africa has been limited, due in part to an insufficient understanding of effective strategies for implementation.
Objective: This study aimed to conduct a multi-level formative evaluation identifying barriers and facilitators for implementing telehealth among health service providers and patients in Central Uganda.
Factors Influencing Knowledge-Action Gap in Patients With Metabolic Dysfunction-Associated Fatty Liver Disease: A Qualitative Study.
J Nutr Educ Behav
January 2025
Department of Fundamental and Community Nursing, School of Nursing, Nanjing Medical University, Nanjing, Jiangsu, China. Electronic address:
Objective: To explore the knowledge-action gap regarding health behaviors and their influencing factors among patients with metabolic dysfunction-associated fatty liver disease (MAFLD), using the Health Belief Model as a theoretical framework.
Design: A qualitative approach was adopted, involving semistructured interviews with individuals with MAFLD.
Setting: Participants were recruited from a community hospital and a tertiary hospital in Nanjing, China, between July and October 2022.
Differences between private and public primary health care centers and differences between men and women in antihypertensive care and cardiovascular prevention in all patients with hypertension treated in primary care in Stockholm County, Sweden.
BMC Prim Care
January 2025
Division of Family Medicine and Primary Care, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Huddinge, Sweden.
Aims: To study differences in cardiovascular prevention and hypertension management in primary care in men and women, with comparisons between public and privately operated primary health care (PHC).
Methods: We used register data from Region Stockholm on collected prescribed medication and registered diagnoses, to identify patients aged 30 years and above with hypertension. Age-adjusted logistic regression was used to calculate odds ratios (ORs) with 99% confidence intervals (99% CIs) using public PHC centers as referents.
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