Engaging with patients with diabetes: the role of social media in low-income healthcare organisations.

Background: Type 2 diabetes is the fastest-growing global health concern, and its global prevalence is projected to affect 643 million individuals by 2030. Social media platforms, like Facebook, have become crucial channels for healthcare organisations to engage with the public to promote prevention and disease management, especially in low-resource settings like Honduras. This study aims to perform a retrospective analysis of Honduran healthcare organisations' Facebook posts to understand how effectively they engage diabetes-related content with their followers.

A nationwide cross-sectional study in Saudi Arabia for the assessment of understanding and practices of clinicians towards personalized genetic testing.

In order to plan and facilitate the culture of personalized / precision medicine in medical practices within any healthcare institution, it is requisite for healthcare professionals like clinicians to have a clear understanding and approach towards the practices of personalized genetic testing. This nationwide cross-sectional study aimed to measure the perceptions and knowledge of clinicians towards personalized genetic testing and assess their current practices of personalized genetic testing in clinical settings through an online self-administered questionnaire in Saudi Arabia. The results of the study revealed that almost two-fifths of participants were responsible for ordering genetic tests directly (39.

Digital Technology Enablers of Tele-Neurorehabilitation in Pre- and Post-COVID-19 Pandemic Era - A Scoping Review.

Neurorehabilitation (NR), a major component of neurosciences, is the process of restoring a patient's damaged/disorganized neurological function, through training, therapy, and education, while focusing on patient's independence and well-being. Since the advent of the COVID-19 pandemic, various applications of telecare and telehealth services surged drastically and became an integral part of current clinical practices. Tele-Neurorehabilitation (TNR) is one of such applications.

A scoping review of machine learning for sepsis prediction- feature engineering strategies and model performance: a step towards explainability.

Background: Sepsis, an acute and potentially fatal systemic response to infection, significantly impacts global health by affecting millions annually. Prompt identification of sepsis is vital, as treatment delays lead to increased fatalities through progressive organ dysfunction. While recent studies have delved into leveraging Machine Learning (ML) for predicting sepsis, focusing on aspects such as prognosis, diagnosis, and clinical application, there remains a notable deficiency in the discourse regarding feature engineering.

Incidence and Mortality of Malignant Brain Tumors after 20 Years of Mobile Use.

(1) Objective: This population-based study was performed to examine the trends of incidence and deaths due to malignant neoplasm of the brain (MNB) in association with mobile phone usage for a period of 20 years (January 2000-December 2019) in Taiwan. (2) Methods: Pearson correlation, regression analysis, and joinpoint regression analysis were used to examine the trends of incidence of MNB and deaths due to MNB in association with mobile phone usage. (3) Results: The findings indicate a trend of increase in the number of mobile phone users over the study period, accompanied by a slight rise in the incidence and death rates of MNB.

Assessment of effects of moon phases on hospital outpatient visits: An observational national study.

Objectives: A vast amount of literature has been conducted for investigating the association of different lunar phases with human health; and it has mixed reviews for association and non-association of diseases with lunar phases. This study investigates the existence of any impact of moon phases on humans by exploring the difference in the rate of outpatient visits and type of diseases that prevail in either non-moon or moon phases.

Methods: We retrieved dates of non-moon and moon phases for eight years (1st January 2001-31st December 2008) from the timeanddate.

The landscape of biomedical research progress, challenges and prospects in Saudi Arabia-A systematic review.

Introduction: The main objective of this review was to synthesize the progress, challenges and prospects of biomedical research in Saudi Arabia in order to provide a holistic view to all stakeholders, such as policy makers, decision makers, and local researchers along with external collaborators interested in the field of biomedical research in this region.

Methods: A systematic review was conducted using the scientific literature for bibliometric studies in the field of biomedical research in Saudi Arabia that comprehensively covered past few decades using PubMed. The search was performed by combining verified Medical Subject Heading (MeSH) terms: "biomedical research", "bibliometrics", "Saudi Arabia" using boolean operator "AND".

Deep Learning Prediction Model for Patient Survival Outcomes in Palliative Care Using Actigraphy Data and Clinical Information.

(1) Background: Predicting the survival of patients in end-of-life care is crucial, and evaluating their performance status is a key factor in determining their likelihood of survival. However, the current traditional methods for predicting survival are limited due to their subjective nature. Wearable technology that provides continuous patient monitoring is a more favorable approach for predicting survival outcomes among palliative care patients.

Investigating Students' Perceptions towards Artificial Intelligence in Medical Education.

Implementing a reform in medical education requires students' awareness regarding the importance of artificial intelligence (AI) in modern medical practice. The objective of this study was to investigate students' perceptions of AI in medical education. A cross-sectional survey was conducted from June 2021 to November 2021 using an online questionnaire to collect data from medical students in the Faculty of Medicine at Kuwait University, Kuwait.

A Scoping Review and a Taxonomy to Assess the Impact of Mobile Apps on Cancer Care Management.

Mobile Health (mHealth) has a great potential to enhance the self-management of cancer patients and survivors. Our study aimed to perform a scoping review to evaluate the impact and trends of mobile application-based interventions on adherence and their effects on health outcomes among the cancer population. In addition, we aimed to develop a taxonomy of mobile-app-based interventions to assist app developers and healthcare researchers in creating future mHealth cancer care solutions.

A scoping review to assess the effects of virtual reality in medical education and clinical care.

Due to the challenges and restrictions posed by COVID-19 pandemic, technology and digital solutions played an important role in the rendering of necessary healthcare services, notably in medical education and clinical care. The aim of this scoping review was to analyze and sum up the most recent developments in Virtual Reality (VR) use for therapeutic care and medical education, with a focus on training medical students and patients. We identified 3743 studies, of which 28 were ultimately selected for the review.

Exploring the Potential Use of Wearable Devices as a Prognostic Tool among Patients in Hospice Care.

Smartphones and wearable devices have become a part and parcel of the healthcare industry. The use of wearable technology has already proved its potentials in improving healthcare research, clinical work, and patient care. The real time data allows the care providers to monitor the patients' symptoms remotely, prioritize the patients' visits, assist in decision-making, and carry out advanced care planning.

A Machine Learning Model to Predict Length of Stay and Mortality among Diabetes and Hypertension Inpatients.

: Taiwan is among the nations with the highest rates of Type 2 Diabetes Mellitus (T2DM) and Hypertension (HTN). As more cases are reported each year, there is a rise in hospital admissions for people seeking medical attention. This creates a burden on hospitals and affects the overall management and administration of the hospitals.

Prevalence of obesity in treated and untreated patients with attention deficit hyperactivity disorder: A meta-analysis.

Objectives: To discuss and summarize the scholarly published literature on the difference in obesity rate in treated and untreated attention deficit hyperactivity disorder (ADHD) patients to evaluate the influence of ADHD medication on weight status in these individuals.

Methods: PubMed, Cochrane Library, and Google Scholar databases were searched for eligible articles from January to December 2020 using the following medical subject headings (MeSH) terms: "attention deficit hyperactivity disorder and other hyperactivity disorders", "obesity and overweight", "obesity treatment".

Results: A total of 19,449 study participants included in selected 8 studies were assessed with respect to the prevalence of obesity in medicated and unmedicated subgroups of ADHD patients.

COVID-19 infodemic and digital health literacy in vulnerable populations: A scoping review.

Background: People from lower and middle socioeconomic classes and vulnerable populations are among the worst affected by the COVID-19 pandemic, thus exacerbating disparities and the digital divide.

Objective: To draw a portrait of e-services as a digital approach to support digital health literacy in vulnerable populations amid the COVID-19 infodemic, and identify the barriers and facilitators for their implementation.

Methods: A scoping review was performed to gather published literature with a broad range of study designs and grey literature without exclusions based on country of publication.

Information Flow and Data Gaps in COVID-19 Recording and Reporting at National and Provincial Levels in Indonesia.

Epidemiological surveillance is an essential component of public health practice especially during infectious disease outbreaks. It is critical to offer transparent epidemiological information in a rigorous manner at different regional levels in countries for managing the outbreak situations. The objectives of this research are to better understand the information flow of COVID-19 health monitoring systems and to determine the data gaps of COVID-19 incidence at the national and provincial levels in Indonesia.

Author Correction: Artificial Intelligence based Models for Screening of Hematologic Malignancies using Cell Population Data.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Using machine learning models to predict the initiation of renal replacement therapy among chronic kidney disease patients.

Starting renal replacement therapy (RRT) for patients with chronic kidney disease (CKD) at an optimal time, either with hemodialysis or kidney transplantation, is crucial for patient's well-being and for successful management of the condition. In this paper, we explore the possibilities of creating forecasting models to predict the onset of RRT 3, 6, and 12 months from the time of the patient's first diagnosis with CKD, using only the comorbidities data from National Health Insurance from Taiwan. The goal of this study was to see whether a limited amount of data (including comorbidities but not considering laboratory values which are expensive to obtain in low- and medium-income countries) can provide a good basis for such predictive models.

Artificial Intelligence based Models for Screening of Hematologic Malignancies using Cell Population Data.

Cell Population Data (CPD) provides various blood cell parameters that can be used for differential diagnosis. Data analytics using Machine Learning (ML) have been playing a pivotal role in revolutionizing medical diagnostics. This research presents a novel approach of using ML algorithms for screening hematologic malignancies using CPD.

Data Analytics and Applications of the Wearable Sensors in Healthcare: An Overview.

Improving health and lives of people is undoubtedly one of the prime goals of healthcare organizations, policy-makers, and leaders around the world. [..

Design of Biomedical Informatics Framework for Personalized Medicine in Healthcare Organizations.

To implement personalized medicine effectively at organizational level, it is vital to identify, organize, integrate and leverage multi-dimensional patient data from heterogeneous and distributed resources within an organization. This paper presents the design of a novel informatics framework, to identify, organize and integrate patient's clinical, genomics and environmental data from existing clinical and biomedical resources, and to explore how this patient's data can be leveraged by informatics tools to achieve the goal of personalized medicine.

Graft Rejection Prediction Following Kidney Transplantation Using Machine Learning Techniques: A Systematic Review and Meta-Analysis.

Kidney transplantation is recommended for patients with End-Stage Renal Disease (ESRD). However, complications, such as graft rejection are hard to predict due to donor and recipient variability. This study discusses the role of machine learning (ML) in predicting graft rejection following kidney transplantation, by reviewing the available related literature.