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Fulltext Early-stage pregnancy recognition on microblogs: Machine learning and lexicon-based approaches

Sarsam SM, Alzahrani AI, Al-Samarraie H

Heliyon, 2023 Sep;9(9):e20132.
PMID: 37809524 DOI: 10.1016/j.heliyon.2023.e20132

Pregnancy carries high medical and psychosocial risks that could lead pregnant women to experience serious health consequences. Providing protective measures for pregnant women is one of the critical tasks during the pregnancy period. This study proposes an emotion-based mechanism to detect the early stage of pregnancy using real-time data from Twitter. Pregnancy-related emotions (e.g., anger, fear, sadness, joy, and surprise) and polarity (positive and negative) were extracted from users' tweets using NRC Affect Intensity Lexicon and SentiStrength techniques. Then, pregnancy-related terms were extracted and mapped with pregnancy-related sentiments using part-of-speech tagging and association rules mining techniques. The results showed that pregnancy tweets contained high positivity, as well as significant amounts of joy, sadness, and fear. The classification results demonstrated the possibility of using users' sentiments for early-stage pregnancy recognition on microblogs. The proposed mechanism offers valuable insights to healthcare decision-makers, allowing them to develop a comprehensive understanding of users' health status based on social media posts.
Telemedicine in Middle Eastern countries: Progress, barriers, and policy recommendations

Al-Samarraie H, Ghazal S, Alzahrani AI, Moody L

Int J Med Inform, 2020 09;141:104232.
PMID: 32707430 DOI: 10.1016/j.ijmedinf.2020.104232

BACKGROUND: Despite attempts to reform the healthcare delivery system in the Middle East, expectations for its progress have been-and for some still are-somewhat slow.
OBJECTIVE: This study reviewed progress in the use and adoption of telemedicine in Middle Eastern countries. The key dimensions affecting the progress of telemedicine in these countries were identified.
METHOD: A systematic review of the literature was conducted on 43 peer reviewed articles from 2010 to 2020. The review followed the scientific process of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines of identification, selection, assessment, synthesis, and interpretation of findings.
RESULTS: The results showed that progress made in the utilization of telemedicine was insufficient and varies across Middle Eastern countries. Certain cultural, financial, organizational, individual, technological, legal, and regulatory challenges were found to prevent telemedicine from being fully used to the point where the full range of medical services can be provided. For example, doctor and patient resistance, poor infrastructure, lack of funding, poor system quality, and lack of information technology training were associated with the low adoption of telemedicine in the region.
CONCLUSION: This review provides a number of recommendations that will help policymakers to move toward the integration of innovative technologies in order to facilitate access to health information, health services, and training. It also recommends that health initiatives should focus on health education and health promotion in order to increase public awareness of the benefits of telemedicine services in the region.
Fulltext A non-invasive machine learning mechanism for early disease recognition on Twitter: The case of anemia

Sarsam SM, Al-Samarraie H, Alzahrani AI, Shibghatullah AS

Artif Intell Med, 2022 Dec;134:102428.
PMID: 36462907 DOI: 10.1016/j.artmed.2022.102428

Social media sites, such as Twitter, provide the means for users to share their stories, feelings, and health conditions during the disease course. Anemia, the most common type of blood disorder, is recognized as a major public health problem all over the world. Yet very few studies have explored the potential of recognizing anemia from online posts. This study proposed a novel mechanism for recognizing anemia based on the associations between disease symptoms and patients' emotions posted on the Twitter platform. We used k-means and Latent Dirichlet Allocation (LDA) algorithms to group similar tweets and to identify hidden disease topics. Both disease emotions and symptoms were mapped using the Apriori algorithm. The proposed approach was evaluated using a number of classifiers. A higher prediction accuracy of 98.96 % was achieved using Sequential Minimal Optimization (SMO). The results revealed that fear and sadness emotions are dominant among anemic patients. The proposed mechanism is the first of its kind to diagnose anemia using textual information posted on social media sites. It can advance the development of intelligent health monitoring systems and clinical decision-support systems.
Fulltext The Impact of Social Media Shared Health Content on Protective Behavior against COVID-19

Mohammed F, Al-Kumaim NH, Alzahrani AI, Fazea Y

Int J Environ Res Public Health, 2023 Jan 18;20(3).
PMID: 36767142 DOI: 10.3390/ijerph20031775

The use of social media has increased during the COVID-19 pandemic because people are isolated and working from home. The use of social media enhances information exchange in society and may influence public protective behavior against the COVID-19 pandemic. The purpose of this study is to identify the factors affecting public protective behavior when relying on COVID-19 pandemic-related content shared on social media. A model based on Protection Motivation Theory (PMT) was proposed and validated using a quantitative survey approach. A questionnaire was distributed to random respondents, and 488 responses were received and analyzed using Smart-PLS software. The findings showed that perceived risk, e-health literacy, public awareness, and health experts' participation influence public protective behavior when using social media to share COVID-19-relevant content. The outcomes of this study can enhance government agencies' and public health care authorities' understanding of how to use social media to raise awareness and reduce panic among the public.
Fulltext Run! This road has no ending! A systematic review of PLS-SEM application in strategic management research among developing nations

Shela V, Ramayah T, Aravindan KL, Ahmad NH, Alzahrani AI

Heliyon, 2023 Dec;9(12):e22476.
PMID: 38125546 DOI: 10.1016/j.heliyon.2023.e22476

Partial least squares structural equation modelling (PLS-SEM) is emerging as a prominent methodological tool in strategic management research. Although it offers various advancements to stay relevant with growing research needs, the pace of PLS-SEM adoption may differ in different parts of the world. In this paper, we conducted a systematic review using the PRISMA framework and extracted from the top-ranking strategic management journals 120 articles published between 2011 and 2022 that presented a microscopic view on developing nations. Our findings reveal that despite the astounding methodological solutions offered by PLS-SEM, the studies from developing nations are still trailing behind developed nations in terms of fully exploiting the advancements of PLS-SEM to provide substantial insights to strategic management literature. This review identifies discrepancies in the current application of the method, discusses the most recent advancements and provides the best practices, standard guidelines and recommendations for the best use of PLS-SEM in strategic management research.
Fulltext Customer relationship management systems (CRMS) in the healthcare environment: A systematic literature review

Baashar Y, Alhussian H, Patel A, Alkawsi G, Alzahrani AI, Alfarraj O, et al.

Comput Stand Interfaces, 2020 Aug;71:103442.
PMID: 34170994 DOI: 10.1016/j.csi.2020.103442

Customer relationship management (CRM) is an innovative technology that seeks to improve customer satisfaction, loyalty, and profitability by acquiring, developing, and maintaining effective customer relationships and interactions with stakeholders. Numerous researches on CRM have made significant progress in several areas such as telecommunications, banking, and manufacturing, but research specific to the healthcare environment is very limited. This systematic review aims to categorise, summarise, synthesise, and appraise the research on CRM in the healthcare environment, considering the absence of coherent and comprehensive scholarship of disparate data on CRM. Various databases were used to conduct a comprehensive search of studies that examine CRM in the healthcare environment (including hospitals, clinics, medical centres, and nursing homes). Analysis and evaluation of 19 carefully selected studies revealed three main research categories: (i) social CRM 'eCRM'; (ii) implementing CRMS; and (iii) adopting CRMS; with positive outcomes for CRM both in terms of patients relationship/communication with hospital, satisfaction, medical treatment/outcomes and empowerment and hospitals medical operation, productivity, cost, performance, efficiency and service quality. This is the first systematic review to comprehensively synthesise and summarise empirical evidence from disparate CRM research data (quantitative, qualitative, and mixed) in the healthcare environment. Our results revealed that substantial gaps exist in the knowledge of using CRM in the healthcare environment. Future research should focus on exploring: (i) other potential factors, such as patient characteristics, culture (of both the patient and hospital), knowledge management, trust, security, and privacy for implementing and adopting CRMS and (ii) other CRM categories, such as mobile CRM (mCRM) and data mining CRM.
Fulltext Determinants of cloud computing integration and its impact on sustainable performance in SMEs: An empirical investigation using the SEM-ANN approach

Al-Sharafi MA, Iranmanesh M, Al-Emran M, Alzahrani AI, Herzallah F, Jamil N

Heliyon, 2023 May;9(5):e16299.
PMID: 37251849 DOI: 10.1016/j.heliyon.2023.e16299

Although extant literature has thoroughly investigated the incorporation of cloud computing services, examining their influence on sustainable performance, particularly at the organizational level, is insufficient. Consequently, the present research aims to assess the factors that impact the integration of cloud computing within small and medium-sized enterprises (SMEs) and its subsequent effects on environmental, financial, and social performance. The data were collected from 415 SMEs and were analyzed using a hybrid SEM-ANN approach. PLS-SEM results indicate that relative advantage, complexity, compatibility, top management support, cost reduction, and government support significantly affect cloud computing integration. This study also empirically demonstrated that SMEs could improve their financial, environmental, and social performance by integrating cloud computing services. ANN results show that complexity, with a normalized importance (NI) of 89.14%, is ranked the first among other factors affecting cloud computing integration in SMEs. This is followed by cost reduction (NI = 82.67%), government support (NI = 73.37%), compatibility (NI = 70.02%), top management support (NI = 52.43%), and relative advantage (NI = 48.72%). Theoretically, this study goes beyond examining the determinants affecting cloud computing integration by examining their impact on SMEs' environmental, financial, and social performance in a comprehensive manner. The study also provides several practical implications for policymakers, SME managers, and cloud computing service providers.
Digital Innovations for Occupational Safety: Empowering Workers in Hazardous Environments

Dodoo JE, Al-Samarraie H, Alzahrani AI, Lonsdale M, Alalwan N

Workplace Health Saf, 2024 Jan 09.
PMID: 38193448 DOI: 10.1177/21650799231215811

BACKGROUND: The quest to increase safety awareness, make job sites safer, and promote decent work for all has led to the utilization of digital technologies in hazardous occupations. This study investigated the use of digital innovations for safety and health management in hazardous industries. The key challenges and recommendations associated with such use were also explored.
METHOD: Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, a total of 48 studies were reviewed to provide a framework for future pathways for the effective implementation of these innovations.
FINDINGS: The results revealed four main categories of digital safety systems: wearable-based systems, augmented/virtual reality-based systems, artificial intelligence-based systems, and navigation-based systems. A wide range of technological, behavioral, and organizational challenges were identified in relation to the key themes.
CONCLUSION: Outcomes from this review can inform policymakers and industrial decision-makers about the application of digital innovations for best safety practices in various hazardous work conditions.