scholarly journals Artificial Intelligence Is Reshaping Healthcare Amid Covid-19: A Review in the Context of Diagnosis & Prognosis

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1604
Author(s):  
Rajnandini Saha ◽  
Satyabrata Aich ◽  
Sushanta Tripathy ◽  
Hee-Cheol Kim
Keyword(s):  
Artificial Intelligence ◽  
Structural Model ◽  
Nature Medicine ◽  
Healthcare Sector ◽  
Successful Implementation ◽  
Factors Affecting ◽  
Diagnosis And Prognosis ◽  
Effective Interventions ◽  
Novel Coronavirus ◽  
Interpretive Structural Model

Preventing respiratory failure is crucial in a large proportion of COVID-19 patients infected with SARS-CoV-2 virus pneumonia termed as Novel Coronavirus Pneumonia (NCP). Rapid diagnosis and detection of high-risk patients for effective interventions have been shown to be troublesome. Using a large, computed tomography (CT) database, we developed an artificial intelligence (AI) parameter to diagnose NCP and distinguish it from other kinds of pneumonia and traditional controls. The literature was studied and analyzed from diverse assets which include Scopus, Nature medicine, IEEE, Google scholar, Wiley Library, and PubMed. The search terms used were ‘Covid-19’, ‘AI’, ‘diagnosis’, and ‘prognosis’. To strengthen the overall performance of AI in COVID-19 diagnosis and prognosis, we segregated several components to perceive threats and opportunities, as well as their inter-dependencies that affect the healthcare sector. This paper seeks to pick out the crucial fulfillment of factors for AI with inside the healthcare sector in the Indian context. Using critical literature review and experts’ opinion, a total of 11 factors affecting COVID-19 diagnosis and prognosis were detected, and we eventually used an interpretive structural model (ISM) to build a framework of interrelationships among the identified factors. Finally, the matrice d’impacts croisés multiplication appliquée á un classment (MICMAC) analysis resulted the driving and dependence powers of these identified factors. Our analysis will help healthcare stakeholders to realize the requirements for successful implementation of AI.

scholarly journals Factors Affecting the Lut Desert Tourism in Iran: Developing an Interpretive-Structural Model

2021 ◽  
Vol 13 (13) ◽  
pp. 7245
Author(s):  
Beniamino Murgante ◽  
Mohammad Eskandari Sani ◽  
Sara Pishgahi ◽  
Moslem Zarghamfard ◽  
Fatemeh Kahaki
Keyword(s):  
Sampling Method ◽  
Structural Model ◽  
Tourism Development ◽  
Cost Effective ◽  
Structural Modeling ◽  
Interpretive Structural Modeling ◽  
Factors Affecting ◽  
Purposive Sampling ◽  
Interpretive Structural Model ◽  
Lut Desert

The Lut desert is one of the largest and most attractive deserts in Iran. The value of desert tourism remains unclear for Iran’s economy and has only recently been taken into consideration by the authorities, although its true national and international value remains unclear. This study was aimed at investigating the factors that influence tourism development in the Lut desert. Data collected through the purposive sampling method was analyzed using Interpretive Structural Modeling and the MICMAC Analysis. According to the results, cost-effective travel expenses, security, and safety provided in the desert, together with appropriate media advertising and illustration of the Lut desert (branding) are the leading factors that influence tourism in the Lut desert in Iran. This paper highlighted the importance of desert tourism, especially in this region.

The Interpretive Structural Model of EPC Project Risk Factors

2014 ◽  
Vol 513-517 ◽  
pp. 3269-3272
Author(s):  
Jing Min Wang ◽  
Yan Mei Li ◽  
Yi Ping Zhu
Keyword(s):  
Risk Factors ◽  
Structural Model ◽  
Energy Performance ◽  
Performance Contracting ◽  
Factors Affecting ◽  
Policies And Measures ◽  
Project Risks ◽  
Interpretive Structural Model ◽  
The Relationship

Project invested with a type of Energy Performance Contracting has many stakeholders and its structure is complex. Risk factors produced from it are so widespread that its hard to control the risks of EPC project. Analyzing EPC project risks with interpretive structural model, solving reachability matrix and establishing Interpretive Structural Model to identify the relationship between factors and the surface, middle and deep risk factors affecting EPC project based on determination of risks, which provides a reference for policies and measures formulation of the relevant departments.

scholarly journals Artificial Intelligence Technology for COVID-19 Detection at Early Stage with Special Reference to Health Disparity and Socioeconomic Status

2020 ◽  
Vol 1 (1) ◽  
pp. 01-02
Author(s):  
Prabir Mandal
Keyword(s):  
Artificial Intelligence ◽  
Survival Rate ◽  
Minority Groups ◽  
Early Stage ◽  
Ethnic Minority Groups ◽  
Diagnosis And Prognosis ◽  
Artificial Intelligence Technology ◽  
Novel Coronavirus ◽  
Median Survival Rate ◽  
Racial And Ethnic Minority

A novel coronavirus, SARS-CoV-2, was identified as the cause of an outbreak of viral pneumonia in Wuhan, China. The disease, later named coronavirus disease 2019 (COVID-19), subsequently spread globally. COVID- 19 is an aggressive disease with a low median survival rate. Ironically, the treatment process is long and very costly due to its high recurrence and mortality rates. Accurate early diagnosis and prognosis prediction of COVID-19 are essential to enhance the patient's survival rate. Mount Sinai researchers are the first in the country to use artificial intelligence (AI) combined with imaging, and clinical data to analyze patients with COVID-19. They have developed a unique algorithm that can rapidly detect COVID-19 based on how lung disease looks in computed tomography (CT scans) of the chest, in combination with patient information including symptoms, age, bloodwork, and possible contact with someone infected with the virus. AI has huge potential for analyzing large amounts of data quickly, an attribute that can have a big impact in a situation such as a pandemic. There is increasing evidence that some racial and ethnic minority groups are being disproportionately affected by COVID-19.

scholarly journals Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments

Symmetry ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 16
Author(s):  
Abdul Majeed ◽  
Seong Oun Hwang
Keyword(s):  
Artificial Intelligence ◽  
Personal Data ◽  
General Purpose ◽  
Data Driven ◽  
Healthcare Sector ◽  
Future Research ◽  
Recent Developments ◽  
Technological Developments ◽  
Novel Coronavirus

This paper presents the role of artificial intelligence (AI) and other latest technologies that were employed to fight the recent pandemic (i.e., novel coronavirus disease-2019 (COVID-19)). These technologies assisted the early detection/diagnosis, trends analysis, intervention planning, healthcare burden forecasting, comorbidity analysis, and mitigation and control, to name a few. The key-enablers of these technologies was data that was obtained from heterogeneous sources (i.e., social networks (SN), internet of (medical) things (IoT/IoMT), cellular networks, transport usage, epidemiological investigations, and other digital/sensing platforms). To this end, we provide an insightful overview of the role of data-driven analytics leveraging AI in the era of COVID-19. Specifically, we discuss major services that AI can provide in the context of COVID-19 pandemic based on six grounds, (i) AI role in seven different epidemic containment strategies (a.k.a non-pharmaceutical interventions (NPIs)), (ii) AI role in data life cycle phases employed to control pandemic via digital solutions, (iii) AI role in performing analytics on heterogeneous types of data stemming from the COVID-19 pandemic, (iv) AI role in the healthcare sector in the context of COVID-19 pandemic, (v) general-purpose applications of AI in COVID-19 era, and (vi) AI role in drug design and repurposing (e.g., iteratively aligning protein spikes and applying three/four-fold symmetry to yield a low-resolution candidate template) against COVID-19. Further, we discuss the challenges involved in applying AI to the available data and privacy issues that can arise from personal data transitioning into cyberspace. We also provide a concise overview of other latest technologies that were increasingly applied to limit the spread of the ongoing pandemic. Finally, we discuss the avenues of future research in the respective area. This insightful review aims to highlight existing AI-based technological developments and future research dynamics in this area.

scholarly journals Exploring Challenges to Implementation of IT Service Management System ISO 20000: Implications in Managing Big Data in Emerging Economy

2019 ◽  
Author(s):  
Nafis Ahmad ◽  
Md. Golam Rabbany ◽  
Syed Mithun Ali
Keyword(s):  
Big Data ◽  
Management System ◽  
Service Management ◽  
Structural Model ◽  
Emerging Economy ◽  
Successful Implementation ◽  
It Service Management ◽  
It Service ◽  
It Support ◽  
Interpretive Structural Model

This study contributes to the literature by exploring challenges to implementing ISO 20000-1 in an emerging economy context, and suggests ways to overcome these challenges. A survey-based methodology was adopted. The data were analyzed using principal component analysis. The results indicated that senior management support was the most significant challenge for the successful implementation of IT Service Management (ITSM) systems. Other significant challenges were the justification of significant investment, premium customer support, co-operation and co-ordination among IT support teams, proper documentation, and effective process design The findings help managers introduce IT service management system (ISO 20000-1:2011) as well as improving IT service delivery system in IT support organizations for managing big data in an emerging economy. In the future, cross-firm and cross-country studies on challenges to ISO 20000 can be conducted. Also, interpretive structural model (ISM) can be formulated to examine the interrelationships among the identified challenges to ISO 20000.

scholarly journals An ISM Approach for the Barrier Analysis in Implementing Green Campus Operations: Towards Higher Education Sustainability

2020 ◽  
Vol 12 (1) ◽  
pp. 363 ◽  
Author(s):  
Hamed Gholami ◽  
Mohamad Faizal Bachok ◽  
Muhamad Zameri Mat Saman ◽  
Dalia Streimikiene ◽  
Safian Sharif ◽  
...  
Keyword(s):  
Structural Model ◽  
Mutual Influence ◽  
Resistance To Change ◽  
Methodological Approach ◽  
Successful Implementation ◽  
Interpretive Structural Modeling ◽  
Research Findings ◽  
Green Campus ◽  
The University ◽  
Interpretive Structural Model

Although various initiatives have been undertaken by the universities worldwide to ensure that their campus operates sustainably, there are emergent barriers that pose serious challenges to the practitioners and subsequently hinder the successful implementation. The research for this paper was built upon the discussion concerning ‘campus operations’, which is one of the dominant sustainability elements in the university systems. It analyzes the barriers for green campus operations implementation through a methodological approach, which was implemented in two tiers. For identification of the barriers, a comprehensive review of the literature was performed and consulted with academic experts who have been involved in greening campus operations in the university. Next, interpretive structural modeling was used to analyze and develop a model of interactions, mutual influence, and relationship among barriers. The results revealed an eighteen-barrier interpretive structural model with eight levels. The analysis indicated that ‘lack of awareness’, ‘lack of knowledge’, ‘resistance to change’, and ‘inefficient communication’ are the dominant barriers with high driving and low dependence powers. The research findings highlighted the importance of this structural model for universities to facilitate the implementation of campus operations by removing the dominant barriers.

scholarly journals BIM for Deconstruction: An Interpretive Structural Model of Factors Influencing Implementation

Buildings ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 227
Author(s):  
Lovelin Obi ◽  
Bankole Awuzie ◽  
Chukwudi Obi ◽  
Temitope S. Omotayo ◽  
Adekunle Oke ◽  
...  
Keyword(s):  
Construction Industry ◽  
Business Models ◽  
Structural Model ◽  
Factor Model ◽  
Construction And Demolition Waste ◽  
Successful Implementation ◽  
Demolition Waste ◽  
Factors Influencing ◽  
The Uk ◽  
Interpretive Structural Model

Transitioning from demolition to deconstruction practices for end-of-life performances is gaining increasing attention following the need for the construction industry to minimise construction and demolition waste. Building information modelling (BIM) presents an opportunity for sustainable deconstruction. However, the notion of BIM for deconstruction (BIMfD) is still in its infancy in the United Kingdom. Although a few studies on BIMfD are evident, a focus on identifying the underlying factors necessary for successful implementation of BIMfD is lacking. The purpose of this study was to identify and analyse the underlying factors necessary for BIMfD implementation in the UK construction industry. It employed a four-stage research design. The reviewed literature explored extant views on BIM implementation factors to identify an initial list of possible factors influencing BIMfD implementation. Subsequently, a mix of questionnaire, focus group discussions and structured interviews were employed at various stages to refine and contextualise 15 factors necessary for BIMfD implementation in the UK construction industry. The contextual interrelationships among the factors were evaluated using interpretive structured modelling (ISM). This evaluation culminated in a BIMfD implementation factor model. The findings identified BIMfD experts, responsiveness of business models to innovative practices and industry’s acceptance to embrace change as the principal factors influencing BIMfD implementation in the UK. The implications of the findings attest that BIMfD experts and advisors must champion the adoption and implementation of BIMfD in the UK and business models need to become more responsive to accommodate BIMfD innovative practices. A BIMfD framework was conceptualised. Even though the BIMfD framework was designed from the UK perspective, the global construction industry can leverage the outcomes of this study. This paper, therefore, brings to the fore, a hierarchical BIMfD implementation factor model to support improved deconstruction practices in the construction industry.

scholarly journals Interpretive Structural Model Based Factor Analysis of BIM Adoption in Chinese Construction Organizations

2019 ◽  
Vol 11 (7) ◽  
pp. 1982 ◽  
Author(s):  
Guofeng Ma ◽  
Jianyao Jia ◽  
Jiyong Ding ◽  
Shanshan Shang ◽  
Shan Jiang
Keyword(s):  
Structural Model ◽  
Information Modeling ◽  
Top Down ◽  
Project Leadership ◽  
Factors Affecting ◽  
Construction Organizations ◽  
Building Information ◽  
Interpretive Structural Model ◽  
Cross Reference

The adoption of Building Information Modeling (BIM) will definitely improve the efficiency and quality of the AEC (architecture, engineering and construction) industry. However, many factors need to be improved before BIM adoption. Based on the interaction between institutions and technology, factors affecting BIM adoption in AEC organizations, within the context of China, are identified and analyzed. Firstly, 21 factors are identified by literature research. Then, an interpretive structural model (ISM) technique is used to establish a hierarchical structure, and matrix impacts cross-reference multiplication applied to a classification (MICMAC) is used for factor classification. The results indicate that corporate/project leadership and software functionality are the two fundamental factors. What’s more, the dynamic mechanism has gradually changed from top-down to a combination of top-down and bottom-up.

scholarly journals Factors affecting the strategic planning in local organizations for designing a structural model

Ciencia Unemi ◽  
2020 ◽  
Vol 13 (34) ◽  
pp. 1-15
Author(s):  
Behzad Souki ◽  
Reza Najaf Beigi ◽  
Karamullah Daneshfard
Keyword(s):  
Strategic Planning ◽  
Structural Equation ◽  
Structural Model ◽  
Communication Technologies ◽  
Factors Affecting ◽  
Local Organizations ◽  
Acceptable Quality ◽  
Validity Indices ◽  
Interpretive Structural Model

This study aims to identify the problems affecting strategic planning in local organizations which can help the development of the society. By conducting interviews and studying the available sources, 33 factors were identified and presented, and they were subdivided into four major groups of behavior, communication, knowledge and institutions. The classification was designed on the basis on the application of Interpretive Structural Model (ISM) and a qualitative-quantitative method. The Smart PLS software was used as the best structural equation modelling tool to design the study model. The final model was also tested using the technique of the least partial squares. The six factors of political behavior, negotiation experience, communication technologies, relations with extra-organizational agents, regulatory effectiveness and organization were found to be fundamental. The validity of the model included community validity indices and augmentation validity. The model's adequacy indices were also positive and greater than zero, indicating that the model devised has acceptable quality and validity. In addition, the GOF index obtained from the calculations was found to be 0.611, which shows the high quality of the model. The study's assumptions about the effects of the four groups of factors on strategic planning were also attested to with 0.95 possibilities. Finally, based on results gained out of the study, certain strategies were suggested for the local organizations.

Analysis of factors affecting the realization of right to adequate housing in Iran: developing an interpretive-structural model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Moslem Zarghamfard ◽  
Abolfazl Meshkini
Keyword(s):  
Structural Model ◽  
Sampling Technique ◽  
Governance Structure ◽  
Statistical Population ◽  
Content Type ◽  
Factors Affecting ◽  
The Right ◽  
Interpretive Structural Model ◽  
The Relationship ◽  
Practical Implications

Purpose Access to adequate housing represents a right entitled to any individual, as has been acknowledged by all states around the world. In Iran, despite the declaration of this right as per the Article 31 of Iran’s Constitution, it is yet to be realized. The purpose of this study is to developing a model for realization of the right to the adequate housing. Design/methodology/approach The present research was performed through a guided qualitative method. Structural-interpretative modeling was performed to present the model of right to adequate housing, and Mic Mac analysis was conducted for clustering the identified factors. Statistical population was composed of housing experts, with the samples taken via purposive sampling technique. Findings Based on the findings, the following factors were found to impose the largest effects on the realization of the right to adequate housing: alignment with ideology and beliefs of society; governance structure related arrangements; social structure related arrangements; improving security of tenure; justice in tenure; and local requirements. Practical implications Findings of this research contributes to increasing the awareness of the housing officials about their policies, reminding them the necessity of revisiting their routines for policy-setting. Indeed, they must switch from centralized policymaking to localized (provincealization) policymaking. Originality/value To the best of authors’ knowledge, this research is the first to investigate the right to adequate housing in Iran, elaborating on the relationship between relevant theories and practical issues.

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