The CHAMPS Tool is designed to assess the risk of falls among children based on factors such as mental status, history of falls, age, mobility issues, parental involvement, and safety measures. The current study aimed to evaluate nurses' perception of the CHAMPS Tool in the pediatric ward of Hermina Hospital, which is prone to falls. The research involved 30 nurses from the pediatric ward and ICU/PICU of the hospital. The study used a descriptive-analytical research design and a questionnaire instrument to collect data. The validity testing showed that 13 out of 18 statements were valid, and five invalid statements were dropped. The reliability test using Cronbach's Alpha was 0.784 > 0.6. The data analysis used univariate frequency distribution analysis. The study found that all nurses who participated positively perceived the CHAMPS Tool, as they obtained a questionnaire score of ≥39.

Originally developed for aerospace engineering, the Kalman Filter, a mathematical algorithm, has proven to have a novel application in mental health, providing a potential instrument for prediction, monitoring, and personalized treatment of mental health conditions. This paper investigates the theoretical underpinning of the Kalman Filter comparing its application in dynamic systems with the intricate, changing nature of mental health. We analyze the potential of the Kalman Filter to transform mental health care by supplying a quantitative, model-based way of comprehending the complex interaction of biological, psychological, and social factors that determine an individual's mental condition. The Kalman Filter holding promise for mental health interventions to be made more efficient and responsive is illustrated by mood prediction and monitoring, personalized treatment, and cognitive behavioral modification applications. Yet, the issues connected with data confidentiality, the difficulty in human behavior modeling, and the link between qualitative and quantitative data are also considered. The paper ends by covering future perspectives that among other things stress further research aimed at fine-tuning the algorithm's applicability in mental health care and providing a grounding for insights into its possibility to contribute to a more adaptive, personalized and efficient approach to treatment. This investigation at the crossroads of the advanced math theory and mental health care demonstrates the innovative use of the Kalman Filter and provides a promising direction for the future research and practical application in this area.

Background: The development of communication and information technology can positively impact healthcare services. E-puskesmas (E-Public Health Center) is an application developed to meet the needs of digital data recording at Puskesmas (PHC). Objective: This study explored the application of e-Puskesmas in the Public Health Center in Luwu Regency, South Sulawesi, Indonesia. Method: This study used a descriptive qualitative research approach with semi-structured interviews. The participants in the study were 16 Puskesmas (PHC) staff members who were selected via the purposive sampling method. Data analysis adopted a thematic analysis approach. Results: We found in this study that there are three interrelated main themes: (1) Experiences of e-puskesmas implementation, (2) Barriers of e-puskesmas implementation, and (3) Facilitators of e-puskesmas implementation. Conclusion: The study findings indicated that the application of e-Puskesmas in terms of human resources, organization, and technology has not been optimal. There were still some obstacles to e-Puskesmas implementation. A joint commitment is still needed between the health office and the public health centers to overcome the barriers or challenges of e-Puskesmas application in PHC.

Utilizing the Hospital Information System (HIS) facilitates the implementation of quality improvement strategies for hospital services. EMR contributes to service development by facilitating quality, sustainable, integrated, and efficient health care. Objective: This study explored the experience of EMR users in a private hospital with a descriptive qualitative research design. Method: Researchers selected 20 participants and conducted semi-structured interviews with interview guidelines. A descriptive qualitative approach with a thematic analysis was used for data analysis. Results: We found three interconnected main themes: (1) Benefits of implementation of MRE, (2) Barriers to implementation of MRE, and (3) Facilitators to implementation of MRE. Each theme included several subthemes. Conclusion: Aspects such as hardware, finance, human resources, technical support, leadership, and training all contribute to the success of an EMR implementation. EMR facilitates information accessibility, improves the completeness of medical records, and increases the efficacy of the communication process. Hospitals derive immense benefits from EMR implementation; therefore, to achieve high-tech hospitals, the EMR implementation process must maximize the participation and engagement of organizational members under the guidance of strong leadership-several challenges with EMR implementation. Confidentiality must be considered when evaluating EMR security hazards and access rights involving usernames and passwords for logging in and out. According to participants, one of the difficulties of implementing EMR is related to its management.

The pattern of the COVID-19 pandemic has affected many aspects of students’ lives, including their physical, social, and mental well-being. This study aimed to examine the association between virtual learning and feelings of depression among a sample of university students in the Emirate of Sharjah, UAE. Using a descriptive, cross-sectional study design, quantitative data were collected from undergraduate students by means of online surveys. To assess the levels and prevalence od depression among university students, the Zung Self-Rating Depression Scale was used. Our findings revealed that there were statistically significant associations between the individual characteristics and levels of depression in terms of academic level (p < .001), place of residence (p < .001), screen time during virtual learning (p = .019), and mode of course delivery in the spring semester of 2021–2022 (p = .006). Although virtual learning has become a necessary feature of all educational institutions, more attention needs to be paid to its psychological impact.

Massive amounts of data describing the genomic, transcriptomic, and epigenomic features of many different cells are produced by single-cell omics approaches. Artificial intelli- gence (AI) models are widely used to infer biological informa- tion and construct predictive models from these data due to their adaptability, scalability, and remarkable success in other disciplines. Low-dimensional representations of single-cell omics data, batch normalization, cell type classification, trajectory inference, gene regulatory network inference, and multimodal data integration have all seen a recent surge in innovation thanks to machine and deep learning. We provide a survey of recent developments in machine learning (ML) algorithms intended for analysis of single-cell omics data to aid readers in navigating this rapidly-evolving literature.

Medical technologies bolstered by AI are quickly developing into viable options for actual clinical use. Wearable, smartphones, and other mobile monitoring sensors are producing vast amounts of data that can be processed by deep learning algorithms in a variety of medical settings. Patients are eager for augmented medicine to be implemented because it will give them more control over their care and allow them to receive more tailored treatment, but doctors are hesitant to embrace the shift because they are not equipped to deal with the resulting changes in clinical practice. In addition, this phenomenon raises the questions of whether or not these cutting-edge tools should be validated by conventional clinical trials, whether or not medical schools should update their curricula to reflect the rise of digital medicine, and whether or not the ethics of constant connected monitoring should be taken into account. The purpose of this paper is to explore the current research literature and offer a holistic view of the implications of well-established clinical applications of artificial intelligence on medical professionals, hospitals, medical schools, and bioethics.

School nurses have the vital role of providing physical and mental healthcare for children in schools, and need to be well supported. This study aimed to explore the use of an electronic decision support system based on artificial intelligence to assist school nurses in providing care for children in school clinics. The study will use an experimental study design. Satisfaction of students, nurses, and school staff with the nursing provided at school will be measured before and after implementation, and then compared.

In this paper, we proposed four different machine learning classification models, i.e., Logistic Regression, Decision Tree, Multilayer Perceptron, and XGBoost, to predict Blood Pressure levels. Moreover, various performance metrics for each model have been calculated, such as accuracy, specificity, precision, recall, and F1 score. According to the findings and comparison of each classification model, XG-Boost achieves the highest classification accuracy of 90%. In contrast, Multilayer Perceptron, Decision Tree, and Logistic Regression achieved 87.33%, 83.83%, and 73.50% for blood pressure classifications, respectively. This study can forecast blood pressure-related diseases in the medical field.

High blood pressure (BP) or hypertension is a dangerous and deadly condition which can lead to serious disorders and high risk of heart attacks, strokes or death. Therefore, studying and monitoring blood pressure levels is highly important. In this study, we propose four distinct machine learning classification models to predict blood pressure levels. The classifiers used are: Random Forest (RF), CatBoost (CB), Support Vector Machine (SVM), and, K-Nearest Neighbors (KNN). Furthermore, several performance indicators such as accuracy, specificity, precision, recall, and F1 score have been calculated for each model. An accuracy of up to 90% was achieved for CATBoost and RF, and up to 87% and 78.33% for SVM and KNN respectively. This study was able to predict blood pressure-related disorders and cardiovascular diseases.

Electroencephalography (EEG) is a valuable instrument for acquiring brain signals from the scalp surface area that correlate to many states, one of which is epilepsy, which is defined as a central nervous system disorder that generates periods of abnormal brain activity, often known as seizures. Based on the EEG signal, frequencies are ranging from 0.1 Hz to more than 100 Hz, these signals are classified as delta, theta, alpha, beta, and gamma. This paper uses the four features extracted from EEG signal to detect Epileptic Seizures, by using a combination of both Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) mainly using MATLAB. Bonn University EEG database has been used. The data was obtained using 128 channels and is divided into five different data classes: Z, N, O, F, and S. Each dataset class contains 100 segments taken from individual channels with a period of 23.6 seconds, or in other words, each dataset class contains 4097 pulses/samples with a sampling frequency of 173.61 Hz. Important statistical features were computed such as mean, standard deviation, skewness, and kurtosis. In this work only the gamma-band of the decomposed signal is used to extract the four statistical features, two classifiers are applied one to detect epilepsy and one to detect the seizure. the epilepsy classifier is 90.3 % accurate and the seizure classifier is 98.7% accurate.

In this paper, an adaptive formulation of the sliding innovation filter (SIF) is presented. The SIF is a recently proposed estimation strategy that has demonstrated robustness to modeling errors and uncertainties. It utilizes a switching gain that is a function of the innovation (measurement error) and sliding boundary layer term. In this paper, a time-varying sliding boundary layer is derived based on minimizing the state error covariance. The resulting solution creates an adaptive formulation of the SIF. The adaptive SIF is applied on a linear aerospace system, and is compared with the well-known Kalman filter (KF) and the standard SIF. The results demonstrate the robustness of the new estimation strategy in the presence of modeling uncertainties and system faults.

In this paper, a new filter in the nonlinear Kalman filtering framework is proposed. The new filter is referred to as the lattice Kalman filter (LKF) and is based on a class of quasi-Monte Carlo (QMC) methods known as lattice rules. The proposed LKF method uses the Korobov type lattice rule to deterministically generate sample points that are randomly shifted based on the Cranley-Patterson shift method in order to approximate multi-dimensional integrals in the Gaussian filtering context. The mathematical formulation of the proposed LKF method as well as its error bound propagation are discussed. To evaluate the efficiency of the LKF, it is applied on a nonlinear aerospace system and compared with four other well-known methods presented in the literature. Simulation results demonstrate LKF uses significantly fewer sampling points yielding a significantly lower computational burden than another variant of QMC filter while maintaining the estimation accuracy. Furthermore, it provides asymptotically similar results to the unscented Kalman filter (UKF) but with less computational complexity, which is an important consideration in real applications.

World Health Organization reports indicate childhood obesity has reached an epidemic state worldwide. In the United Arab Emirates, 45% of school-aged children are either overweight or obese. Interventional studies to tackle this issue are urgently needed. Researchers have described the childhood and adolescence developmental stages as periods in which habits and skills that persist into adulthood are developed; therefore, this period may be considered a critical time for establishing healthy lifestyles and positive adaptive behaviors (e.g., nutritional behaviors). Currently, most available health, fitness, or lifestyle-related mobile applications are directed to the general population and not tailored to the developing children and adolescent population.

Anxiety disorders are common among children and adolescents because of the nature of their developmental stage, which requires significant skill development and adaptation to many life transitions. A recent cross-sectional study conducted in the United Arab Emirates (UAE) with children aged 10-19 years (N=968) reported the overall prevalence was anxiety disorders of 28%. Of the whole sample, 37.1% had panic disorder, 45.5% had separation anxiety, 20% had social anxiety, and 36.5% had school avoidance. This highlights that age-appropriate initiatives are urgently needed to reduce the high rate of anxiety-related disorders. In the UAE, there are additional risk factors for the development of mental health problems, particularly among young people (i.e., consanguineous marriages, large families, and a large youth population in the active phase of development). This further reinforces the need to actively initiate intervention to address these issues among the youth population before they become entrenched in their adult personalities. According to World Health Organization reports, almost half of mental health conditions develop before age 14 years. The UAE healthcare system has identified a clear need to further develop existing mental health services.

Smoking is a global problem. In the United Arab Emirates (UAE), about 34% of men are smokers, and there are around 3000 smoking-related deaths each year. Smoking is also an increasing problem in the UAE, with smoking/smoking-related products responsible for many fatal diseases, including lung cancer, myocardial infarction, diabetes, and high blood pressure. In addition to these diseases, smoking is a major contributor to air pollution. Many countries offer smoking cessation programs to promote population health, but these programs are often time consuming and costly. In this paper, we proposed a novel method based on a mHealth application that uses customized and automated SMS messages to support smokers to quit smoking. A hybrid motivational theory will be used to motivate participants who are willing to enroll in our program for a 1-year period. During this time, all program users will be monitored and motivated by the mHealth application. The main objective of this project was to develop a framework for use by 200 UAE participants. It is intended that our program will motivate and encourage participants, and build their confidence to quit smoking.