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| http://dx.doi.org/10.1080/15265161.2023.2186512 | DOI Listing |
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Interpretable machine learning models for COPD ease of breathing estimation.
Med Biol Eng Comput
January 2025
IDLab, Ghent University-Imec, Technologiepark-Zwijnaarde 126, Zwijnaarde, Belgium.
Chronic obstructive pulmonary disease (COPD) is a leading cause of death worldwide and greatly reduces the quality of life. Utilizing remote monitoring has been shown to improve quality of life and reduce exacerbations, but remains an ongoing area of research. We introduce a novel method for estimating changes in ease of breathing for COPD patients, using obstructed breathing data collected via wearables.
View Article and Find Full Text PDFHealthcare ethics and artificial intelligence: a UK doctor survey.
BMJ Open
December 2024
Centre for Social Ethics and Policy, Department of Law, School of Social Sciences, University of Manchester, Manchester, UK
Objectives: To survey UK doctors on their uses of artificial intelligence (AI) and of their views on the ethics and regulation of AI in healthcare.
Design: Anonymous cross-sectional e-survey.
Setting: An online survey of UK General Medical Council (GMC) registered doctors.
Refining PREVENT prediction models for 10-year risk of cardiovascular disease using measures of anxiety and depression.
CMAJ
January 2025
Schools of Health and Wellbeing (Nakada, Pell, Ho), and Cardiovascular and Metabolic Health (Welsh, Celis-Morales), University of Glasgow, Glasgow, UK; Human Performance Laboratory, Education, Physical Activity and Health Research Unit (Celis-Morales), Universidad Católica del Maule, Talca, Chile; Centro de Investigación en Medicina de Altura (CEIMA) (Celis-Morales), Universidad Arturo Prat, Iquique, Chile.
Background: Anxiety and depression are associated with cardiovascular disease (CVD). We aimed to investigate whether adding measures of anxiety and depression to the American Heart Association Predicting Risk of Cardiovascular Disease Events (PREVENT) predictors improves the prediction of CVD risk.
Methods: We developed and internally validated risk prediction models using 60% and 40% of the cohort data from the UK Biobank, respectively.
Overcoming the threat of anti-bias interventions: Combining self-report and psychophysiological measures to capture the process of change.
PLoS One
January 2025
Faculty of Social and Behavioural Sciences, Utrecht University, Utrecht, The Netherlands.
Anti-bias interventions do not always have the intended results and can even backfire. In light of research on the psychology of morality, we examined whether confronting people with evidence of their own (group's) bias causes a (psychophysiological) threat response, and how to overcome this. We focused on an intervention addressing gender bias in teacher evaluations.
View Article and Find Full Text PDFPerceptions of Clinical Connectedness Among Hospital Environmental Service Workers.
JAMA Netw Open
January 2025
Department of Public Health and Preventive Medicine, State University New York (SUNY) Upstate Medical University, Syracuse, New York.
Importance: Environmental service workers (ESWs) have a critical role within the hospital infrastructure and are at the frontline of infection prevention. ESWs are highly trained in managing all forms of regulated waste, which includes biohazardous waste, and are responsible for the overall patient experience, janitorial work, and infection prevention. Without environmental services, patients have a 6 times greater risk of being infected by pathogens from patients who previously occupied their room.
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