The growing need to gain efficiencies within a home care setting has prompted home care practitioners to focus on health informatics to address the needs of an aging clientele. The remote and heterogeneous nature of the home care environment necessitates the use of non-intrusive client monitoring and a portable, point-of-care graphical user interface. Using a grounded theory approach, this article examines the simulated use of a graphical user interface by practitioners in a home care setting to explore the salient features of monitoring the activity of home care clients. The results demonstrate the need for simple, interactive displays that can provide large amounts of geographical and temporal data relating to patient activity. Additional emerging themes from interviews indicate that home care professionals would use a graphical user interface of this type for patient education and goal setting as well as to assist in the decision-making process of home care practitioners.
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http://dx.doi.org/10.1177/1460458213511346 | DOI Listing |
JMIR Form Res
December 2024
Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States.
Background: Anxiety disorders are common in alcohol use disorder (AUD) treatment patients. Such co-occurring conditions ("comorbidity") have negative prognostic implications for AUD treatment outcomes, yet they commonly go unaddressed in standard AUD care. Over a decade ago, we developed and validated a cognitive behavioral therapy intervention to supplement standard AUD care that, when delivered by trained therapists, improves outcomes in comorbid patients.
View Article and Find Full Text PDFChemosphere
December 2024
Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, NJ, 07030, USA. Electronic address:
Phosphate (PO(III)) contamination in water bodies poses significant environmental challenges, necessitating efficient and accurate methods to predict and optimize its removal. The current study addresses this issue by predicting the adsorption capacity of PO(III) ions onto biochar-based materials using five probabilistic machine learning models: eXtreme Gradient Boosting LSS (XGBoostLSS), Natural Gradient Boosting, Bayesian Neural Networks (NN), Probabilistic NN, and Monte-Carlo Dropout NN. Utilizing a dataset of 2952 data points with 16 inputs, XGBoostLSS demonstrated the highest R (0.
View Article and Find Full Text PDFJ Neurol Sci
December 2024
Computational and Translational Neuroscience Laboratory, Institute of Cognitive Sciences and Technologies, National Research Council (CTNLab-ISTC-CNR), Via Gian Domenico Romagnosi 18A, Rome 00196, Italy; AI2Life s.r.l., Innovative Start-Up, ISTC-CNR Spin-Off, Via Sebino 32, Rome 00199, Italy. Electronic address:
Alzheimer's disease (AD), the most common neurodegenerative disorder world-wide, presents sex-specific differences in its manifestation and progression, necessitating personalized diagnostic approaches. Current procedures are often costly and invasive, lacking consideration of sex-based differences. This study introduces an explainable machine learning (ML) system to predict and differentiate the progression of AD based on sex, using non-invasive, easily collectible predictors such as neuropsychological test scores and sociodemographic data, enabling its application in every day clinical settings.
View Article and Find Full Text PDFComput Methods Programs Biomed
December 2024
Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy.
Background And Objective: Dysfunction of the right ventricular outflow tract (RVOT) is a common long-term complication following surgical repair in patients with congenital heart disease. Transcatheter pulmonary valve implantation (TPVI) offers a viable alternative to surgical pulmonary valve replacement (SPVR) for treating pulmonary regurgitation but not all RVOT anatomies are suitable for TPVI. To identify a suitable landing zone (LZ) for TPVI, three-dimensional multiphase (4D) computed tomography (CT) is used to evaluate the size, shape, and dynamic behavior of the RVOT throughout the cardiac cycle.
View Article and Find Full Text PDFHealthc Technol Lett
December 2024
ITI/LARSyS, Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal.
A thorough understanding of surgical anatomy is essential for preparing and training medical students to become competent and skilled surgeons. While Virtual Reality (VR) has shown to be a suitable interaction paradigm for surgical training, traditional anatomical VR models often rely on simple labels and arrows pointing to relevant landmarks. Yet, studies have indicated that such visual settings could benefit from knowledge maps as such representations explicitly illustrate the conceptual connections between anatomical landmarks.
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