149 research outputs found
Optimization the initial weights of artificial neural networks via genetic algorithm applied to hip bone fracture prediction
This paper aims to find the optimal set of initial weights to enhance the accuracy of artificial neural networks (ANNs) by using genetic algorithms (GA). The sample in this study included 228 patients with first low-trauma hip fracture and 215 patients without hip fracture, both of them were interviewed with 78 questions. We used logistic regression to select 5 important factors (i.e., bone mineral density, experience of fracture, average hand grip strength, intake of coffee, and peak expiratory flow rate) for building artificial neural networks to predict the probabilities of hip fractures. Three-layer (one hidden layer) ANNs models with back-propagation training algorithms were adopted. The purpose in this paper is to find the optimal initial weights of neural networks via genetic algorithm to improve the predictability. Area under the ROC curve (AUC) was used to assess the performance of neural networks. The study results showed the genetic algorithm obtained an AUC of 0.858±0.00493 on modeling data and 0.802 ± 0.03318 on testing data. They were slightly better than the results of our previous study (0.868±0.00387 and 0.796±0.02559, resp.). Thus, the preliminary study for only using simple GA has been proved to be effective for improving the accuracy of artificial neural networks.This research was supported by the National Science Council (NSC) of Taiwan (Grant no. NSC98-2915-I-155-005), the Department of Education grant of Excellent Teaching Program of Yuan Ze University (Grant no. 217517) and the Center for Dynamical Biomarkers and Translational Medicine supported by National Science Council (Grant no. NSC 100- 2911-I-008-001)
Non-communicable Diseases, Big Data and Artificial Intelligence
This reprint includes 15 articles in the field of non-communicable Diseases, big data, and artificial intelligence, overviewing the most recent advances in the field of AI and their application potential in 3P medicine
Predicting healthcare high-cost users using data mining methods
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe increase in healthcare costs is, perhaps, one of the most important issues that governments and
organizations face nowadays. An ageing population and technological advancements are the key
reasons for this phenomenon. In this scenario, proactive measures are very important. This work
aimed to improve the effectiveness of the prevention by helping the identification of the most
probable high-cost users of health services in future years. Data from 2015 to 2019 of approximately
30,000 Central Bank of Brazil’s Health Program’s enrollees were used to train, validate and test four
types of models, considering the kind of high-cost users (simple or cost-bloomers, i.e., non-high-cost
in previous periods) and the time-span between predictors and the dependent variable (none or one
year), an innovation suggested by other authors. Different percentual cut-off points to define highcost
were used, and up to 67% of high-risk users’ expenses could be correctly captured. Results
confirmed the importance of previous costs data for this kind of prediction and showed that costbloomers
and one-year time-span approaches reach good performance, creating opportunities to
improve users’ health outcomes while contributing to the fiscal sustainability of private and public
health systems
Machine Learning-based Detection of Compensatory Balance Responses and Environmental Fall Risks Using Wearable Sensors
Falls are the leading cause of fatal and non-fatal injuries among seniors worldwide, with serious and costly consequences. Compensatory balance responses (CBRs) are reactions to recover stability following a loss of balance, potentially resulting in a fall if sufficient recovery mechanisms are not activated. While performance of CBRs are demonstrated risk factors for falls in seniors, the frequency, type, and underlying cause of these incidents occurring in everyday life have not been well investigated.
This study was spawned from the lack of research on development of fall risk assessment methods that can be used for continuous and long-term mobility monitoring of the geri- atric population, during activities of daily living, and in their dwellings. Wearable sensor systems (WSS) offer a promising approach for continuous real-time detection of gait and balance behavior to assess the risk of falling during activities of daily living. To detect CBRs, we record movement signals (e.g. acceleration) and activity patterns of four muscles involving in maintaining balance using wearable inertial measurement units (IMUs) and surface electromyography (sEMG) sensors. To develop more robust detection methods, we investigate machine learning approaches (e.g., support vector machines, neural networks) and successfully detect lateral CBRs, during normal gait with accuracies of 92.4% and 98.1% using sEMG and IMU signals, respectively.
Moreover, to detect environmental fall-related hazards that are associated with CBRs, and affect balance control behavior of seniors, we employ an egocentric mobile vision system mounted on participants chest. Two algorithms (e.g. Gabor Barcodes and Convolutional Neural Networks) are developed. Our vision-based method detects 17 different classes of environmental risk factors (e.g., stairs, ramps, curbs) with 88.5% accuracy. To the best of the authors knowledge, this study is the first to develop and evaluate an automated vision-based method for fall hazard detection
Health Care for Older Adults
In recent decades, life expectancy has been increasing. This is a historical milestone in the history of humanity. We have never lived so long before. In these circumstances, giving the best care to older adults efficiently is one of the greatest challenges of developed countries. This book explores different initiatives that result in the improvement of health conditions of older adults, such as multicomponent physical exercise programs, interventions that try to avoid loneliness and social isolation, and multidisciplinary assessment, and the treatment of frailty and other geriatric syndromes, of the elderly in various settings such as the Emergency Unit, Orthogeriatrics, and Oncogeriatrics. This book offers different manuscripts to readers, each trying to improve life satisfaction, quality of life, and life expectancy in older adults in different scenarios. It is up to us to achieve these goals. We are sure that these interesting chapters will contribute to improving clinical practices. Following the completion of the Special Issue "Health Care for Older Adults" for the international Journal of Environmental Research and Public Health, the Guest Editors felt the satisfaction of having reached 18 published manuscripts and the possibility of transforming this volume into a book. This book was born from the need to show how health and social advances have increased human longevity as never before. We live longer, knowing more and more the epigenetic mechanisms of this longevity, as extended aging also coexists with the least favorable aging trajectories. Among them, a syndrome stands out from the gerontological and geriatric perspective: frailty. Due to the pandemic, a social problem has increased its presence in clinical practice: ageism. Older adults have found it difficult to access the necessary clinical resources due to the simple matter of age. However, at this moment, we are able to detect and to reverse frailty. In the same way, we should aim to prevent loneliness and social isolation, involved in social frailty. Geriatric syndromes are underdiagnosed and undertreated, but clinical and geriatric knowledge provide diagnostic tools and non-pharmacological approaches to prevent and to treat them. All health professionals working together in an interdisciplinary team could improve the clinical practices to develop a quality health care for older adults, improving their life satisfaction and quality of life perception too
Serious Games and Mixed Reality Applications for Healthcare
Virtual reality (VR) and augmented reality (AR) have long histories in the healthcare sector, offering the opportunity to develop a wide range of tools and applications aimed at improving the quality of care and efficiency of services for professionals and patients alike. The best-known examples of VR–AR applications in the healthcare domain include surgical planning and medical training by means of simulation technologies. Techniques used in surgical simulation have also been applied to cognitive and motor rehabilitation, pain management, and patient and professional education. Serious games are ones in which the main goal is not entertainment, but a crucial purpose, ranging from the acquisition of knowledge to interactive training.These games are attracting growing attention in healthcare because of their several benefits: motivation, interactivity, adaptation to user competence level, flexibility in time, repeatability, and continuous feedback. Recently, healthcare has also become one of the biggest adopters of mixed reality (MR), which merges real and virtual content to generate novel environments, where physical and digital objects not only coexist, but are also capable of interacting with each other in real time, encompassing both VR and AR applications.This Special Issue aims to gather and publish original scientific contributions exploring opportunities and addressing challenges in both the theoretical and applied aspects of VR–AR and MR applications in healthcare
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