Leveraging artificial intelligence-mediated communication for cancer prevention and control and drug addiction: A systematic review.

Objective: To conduct a systematic review on Artificial Intelligence-Mediated Communication (AIMC) behavioral interventions in cancer prevention/control and substance use.

Methods: Eight databases were searched from 2017 to 2022 using the Population Intervention Control Outcome Study (PICOS) framework. We synthesized findings of AIMC-based interventions for adult populations in cancer prevention/control or substance use, applying SIGN Methodology Checklist 2 for quality assessments and reviewing retention and engagement.

Burden of Transport-Related Injuries in the Eastern Mediterranean Region: A Systematic Analysis for the Global Burden of Disease Study 2017.

Background: Transport-related injuries (TIs) are a substantial public health concern for all regions of the world. The present study quantified the burden of TIs and deaths in the Eastern Mediterranean region (EMR) in 2017 by sex and age.

Methods: TIs and deaths were estimated by age, sex, country, and year using Cause of Death Ensemble modelling (CODEm) and DisMod-MR 2.

Predicting the environmental suitability for onchocerciasis in Africa as an aid to elimination planning.

Recent evidence suggests that, in some foci, elimination of onchocerciasis from Africa may be feasible with mass drug administration (MDA) of ivermectin. To achieve continental elimination of transmission, mapping surveys will need to be conducted across all implementation units (IUs) for which endemicity status is currently unknown. Using boosted regression tree models with optimised hyperparameter selection, we estimated environmental suitability for onchocerciasis at the 5 × 5-km resolution across Africa.

Topic Discovery on Farsi, English, French, and Arabic Tweets Related to COVID-19 Using Text Mining Techniques.

Background: Social networks are a good source for monitoring public health during the outbreak of COVID-19, these networks play an important role in identifying useful information.

Objectives: This study aims to draw a comparison of the public's reaction in Twitter among the countries of West Asia (a.k.

CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images.

Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase-polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19.

Estimating global injuries morbidity and mortality: methods and data used in the Global Burden of Disease 2017 study.

Background: While there is a long history of measuring death and disability from injuries, modern research methods must account for the wide spectrum of disability that can occur in an injury, and must provide estimates with sufficient demographic, geographical and temporal detail to be useful for policy makers. The Global Burden of Disease (GBD) 2017 study used methods to provide highly detailed estimates of global injury burden that meet these criteria.

Methods: In this study, we report and discuss the methods used in GBD 2017 for injury morbidity and mortality burden estimation.

Understanding Smartwatch Battery Utilization in the Wild.

Smartwatch battery limitations are one of the biggest hurdles to their acceptability in the consumer market. To our knowledge, despite promising studies analyzing smartwatch battery data, there has been little research that has analyzed the battery usage of a diverse set of smartwatches in a real-world setting. To address this challenge, this paper utilizes a smartwatch dataset collected from 832 real-world users, including different smartwatch brands and geographic locations.

Electronic Medical Records for Mental Disorders: What Data Elements Should These Systems Contain?

Identifying data elements of electronic medical record systems (EMRs) is one of the essential steps for the comprehensive and proper health data collection. The aim of this study was to determine the data elements required for EMRs in the field of mental disorders. We conducted a literature review and also we randomly selected 50 medical records of patients with mental disorders to identify a preliminary list of essential data elements for EMRs for mental disorders.

Indexing Multivariate Mobile Data through Spatio-Temporal Event Detection and Clustering.

Mobile and wearable devices are capable of quantifying user behaviors based on their contextual sensor data. However, few indexing and annotation mechanisms are available, due to difficulties inherent in raw multivariate data types and the relative sparsity of sensor data. These issues have slowed the development of higher level human-centric searching and querying mechanisms.

Energy-Efficient Integration of Continuous Context Sensing and Prediction into Smartwatches.

As the availability and use of wearables increases, they are becoming a promising platform for context sensing and context analysis. Smartwatches are a particularly interesting platform for this purpose, as they offer salient advantages, such as their proximity to the human body. However, they also have limitations associated with their small form factor, such as processing power and battery life, which makes it difficult to simply transfer smartphone-based context sensing and prediction models to smartwatches.