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CLOSER is a computer-based exercise program that aims to improve older adults' health, fitness and social lives. This pilot study aimed to examine the effect of CLOSER, the first computer-based exercise program developed for older adults on a national scale, on those with a history of falls. Forty-eight older adults (71.33 ± 7.47) with a history of falling at least once in the last year were included in the study. Older adults performed CLOSER exercises for (balance maintenance, neck rotation, rhythmic walking, knee flexion and trunk rotation) 2 sessions per week for eight weeks. All individuals were evaluated at baseline and the end of the eighth week. The primary outcome measures were the 30-s Chair-Stand Test (p = 0.002), the Berg Balance Scale (p = 0.002), the Falls Efficacy Scale International (p = 0.003), the Timed Up and Go Test (p = 0.008) and the motivation level (p = 0.007) statistically significant improvements were observed. The results show that a CLOSER-computer-based exercise program effectively increases balance and reduces the risk and fear of falling. In the future, CLOSER could significantly contribute to the healthcare system as an alternative aid for home-based exercise. [ABSTRACT FROM AUTHOR]

Emotion classification is a research field that aims to detect the emotions in a text using machine learning methods. In traditional machine learning (TML) methods, feature engineering processes cause the loss of some meaningful information, and classification performance is negatively affected. In addition, the success of modelling using deep learning (DL) approaches depends on the sample size. More samples are needed for Turkish due to the unique characteristics of the language. However, emotion classification data sets in Turkish are quite limited. In this study, the pretrained language model approach was used to create a stronger emotion classification model for Turkish. Well-known pretrained language models were fine-tuned for this purpose. The performances of these fine-tuned models for Turkish emotion classification were comprehensively compared with the performances of TML and DL methods in experimental studies. The proposed approach provides state-of-the-art performance for Turkish emotion classification. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to predict early graft failure (GF) in patients who underwent Descemet membrane endothelial keratoplasty based on donor characteristics., Methods: Several machine learning methods were trained to predict GF automatically. To predict GF, the following variables were obtained: donor age, sex, systemic diseases, medications, duration of stay in the intensive care unit, death-to-preservation time (DPT), endothelial cell density of the cornea, tightness of Descemet membrane roll during surgery, anterior chamber tamponade, tamponade used for rebubbling, and preoperative best corrected visual acuity. Five classification methods were experimented with the study data set: random forest, support vector machine, k-nearest neighbor, RUSBoosted tree, and neural networks. In holdout validation, 75% of the data were used in training and the remaining 25% used in testing. The predictive accuracy, sensitivity, specificity, f-score, and area under the receiver operating characteristic curve of the methods were evaluated., Results: The highest classification accuracy achieved during the experiments was 96%. The precision, recall, and f1-score values were 0.95, 0.81, and 0.90, respectively. Feature importance was also computed using analysis of variance. The model revealed that GF risk was related to DPT and the intensive care unit duration (P < 0.05). No significant relationship was found between donor age, endothelial cell density, systemic diseases and medications, graft roll, tamponades, and GF risk., Conclusions: This study shows a strong relationship between increased intensive care duration, DPT, and GF. Experimental results demonstrate that machine learning methods may effectively predict GF automatically., Competing Interests: The authors have no funding or conflicts of interest to disclose., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)