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Eliciting Preferences for the Uptake of Smoking Cessation Apps: Discrete Choice Experiment.
J Med Internet Res
January 2025
Behavioural and Implementation Science Group, School of Health Sciences, University of East Anglia, Norwich, United Kingdom.
Background: If the most evidence-based and effective smoking cessation apps are not selected by smokers wanting to quit, their potential to support cessation is limited.
Objective: This study sought to determine the attributes that influence smoking cessation app uptake and understand their relative importance to support future efforts to present evidence-based apps more effectively to maximize uptake.
Methods: Adult smokers from the United Kingdom were invited to participate in a discrete choice experiment.
Predictive Optimization of Patient No-Show Management in Primary Healthcare Using Machine Learning.
J Med Syst
January 2025
Department of Computing, University of North Florida, 1 UNF Dr., Jacksonville, 32246, FL, USA.
The "no-show" problem in healthcare refers to the prevalent phenomenon where patients schedule appointments with healthcare providers but fail to attend them without prior cancellation or rescheduling. In addressing this issue, our study delves into a multivariate analysis over a five-year period involving 21,969 patients. Our study introduces a predictive model framework that offers a holistic approach to managing the no-show problem in healthcare, incorporating elements into the objective function that address not only the accurate prediction of no-shows but also the management of service capacity, overbooking, and idle resource allocation resulting from mispredictions.
View Article and Find Full Text PDFMachine learning prediction model for oral mucositis risk in head and neck radiotherapy: a preliminary study.
Support Care Cancer
January 2025
Oral Diagnosis Department, Faculdade de Odontolodia de Piracicaba, Universidade de Campinas (UNICAMP), Piracicaba, São Paulo, Brazil.
Purpose: Oral mucositis (OM) reflects a complex interplay of several risk factors. Machine learning (ML) is a promising frontier in science, capable of processing dense information. This study aims to assess the performance of ML in predicting OM risk in patients undergoing head and neck radiotherapy.
View Article and Find Full Text PDFBackground: The aim of this study was to explore the value of heparin-binding protein (HBP) in the early recognition of sepsis coagulopathy (SIC) and the prognosis of sepsis patients.
Methods: A retrospective analysis was performed for 139 patients with sepsis admitted to the Intensive Care Unit (ICU) of Hefei Third People's Hospital from April 2022 through April 2024. The clinical baseline data, disease scores [sequential organ failure (SOFA) score, acute physiology and chronic health status (APACHE II) score, and SIC score], inflammatory markers [HBP, procalcitonin (PCT), and interleukin 6 (IL-6)], coagulation-related indexes [platelet count (PLT), prothrombin time (PT), prothrombin time international normalized ratio (PT-INR), activated partial thromboplastin time (APTT), fibrinogen (Fib), and D dimer (D-D)], and the survival time and 28-day prognosis of all patients were observed.
Introduction: Cognitive impairment in older adults is underrecognized in emergency departments. Despite emergency nurses' central role in facilitating ED screening for clinical and social needs, little is known about their perspectives on implementing delirium and dementia screenings. Nurses can provide insights to promote the uptake of these screenings.
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