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Coati optimization algorithm for brain tumor identification based on MRI with utilizing phase-aware composite deep neural network.
Electromagn Biol Med
January 2025
Department of Computer Applications, Kalasalingam Academy of Research and Education - Deemed to be University, Krishnankoil, India.
Brain tumors can cause difficulties in normal brain function and are capable of developing in various regions of the brain. Malignant tumours can develop quickly, pass through neighboring tissues, and extend to further brain regions or the central nervous system. In contrast, healthy tumors typically develop slowly and do not invade surrounding tissues.
View Article and Find Full Text PDFMachine Learning in Optimising Nursing Care Delivery Models: An Empirical Analysis of Hospital Wards.
J Eval Clin Pract
February 2025
Department of Nursing, Trakya University Faculty of Health Sciences, Edirne, Turkey.
Objective: This study aims to assess the performance of machine learning (ML) techniques in optimising nurse staffing and evaluating the appropriateness of nursing care delivery models in hospital wards. The primary outcome measures include the adequacy of nurse staffing and the appropriateness of the nursing care delivery system.
Background: Historical and current healthcare challenges, such as nurse shortages and increasing patient acuity, necessitate innovative approaches to nursing care delivery.
Supervised machine learning and genotype by trait biplot as promising approaches for selection of phytochemically enriched genotypes.
Heliyon
January 2025
Faculty of Engineering, University of Maragheh, Maragheh, Iran.
Sumac is considered as a medicinal and industrial plant. Climate change threats natural ecosystems and hence, evaluation of sumac's genetic diversity, identification of superior genotypes, and conservation of such materials is important. In this study, 5 wild populations of sumac were investigated.
View Article and Find Full Text PDFGeneralizable, sequence-invariant deep learning image reconstruction for subspace-constrained quantitative MRI.
Magn Reson Med
January 2025
Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
Purpose: To develop a deep subspace learning network that can function across different pulse sequences.
Methods: A contrast-invariant component-by-component (CBC) network structure was developed and compared against previously reported spatiotemporal multicomponent (MC) structure for reconstructing MR Multitasking images. A total of 130, 167, and 16 subjects were imaged using T, T-T, and T-T- -fat fraction (FF) mapping sequences, respectively.
Constructing a fall risk prediction model for hospitalized patients using machine learning.
BMC Public Health
January 2025
Department of Pathology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610041, China.
Study Objectives: This study aimed to identify the risk factors associated with falls in hospitalized patients, develop a predictive risk model using machine learning algorithms, and evaluate the validity of the model's predictions.
Study Design: A cross-sectional design was employed using data from the DRYAD public database.
Research Methods: The study utilized data from the Fukushima Medical University Hospital Cohort Study, obtained from the DRYAD public database.
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